Text Analysis of Policies in the Real Estate Market: Comparisons of 21 Chinese Cities

Abstract

Real estate plays a pivotal role in fostering national economic growth and ensuring social stability. In China, housing constitutes the largest fixed asset for the majority of households. Given the extensive network of upstream and downstream industries associated with real estate, the government places significant emphasis on its regulation and development, employing a variety of policy instruments to maintain market stability. This study adopts a quantitative approach to conduct a text analysis of China’s real estate policies through the lens of knowledge mapping and LDA topic modeling, while also comparing policy content across 21 different cities. The findings indicate that real estate policy in China transcends mere market regulation. It also encompasses governance within the construction industry as well as provisions for housing security. Furthermore, due to the diverse roles that real estate plays in economic development and urban construction, there is notable regional heterogeneity in policy priorities. By text analysis of real estate policies, this study provides a systematic overview of policy content, thereby laying a foundation for more nuanced and regionally differentiated research within the realm of real estate policy.

1. Introduction

Focusing on the real estate sector is essential, given its profound influence on both economic performance and broader societal dynamics. Economically, real estate serves as a cornerstone of national development and a major driver of GDP growth. In the United States, for instance, the real estate industry contributes approximately 13% to the nation’s total economic output [1]. Similarly, in China, the sector’s importance is equally evident. By 2023, real estate development enterprises had completed a total investment of ¥68.84 trillion, with the number of such enterprises reaching 100,111 [2]. Beyond its direct economic contributions, the real estate industry also plays a crucial role in employment generation across a wide spectrum of professions—from manual laborers such as construction workers to highly skilled professionals including architects, real estate agents, and property managers [3]. According to the National Bureau of Statistics of China, the average number of employees in China’s real estate sector reached 2.0023 million in 2023 [2]. These figures underscore the sector’s multifaceted significance, making it a key area of interest for economic and policy analysis.

From a social perspective, the real estate sector plays a pivotal role in enhancing societal well-being via the provision of adequate and diverse housing options. In some regions, governments strategically leverage real estate policies as a means to attract skilled talent and stimulate regional development. Moreover, real estate development significantly contributes to urbanization and the modernization of infrastructure, thereby improving the quality of life in urban. It also facilitates sustainable urban growth by promoting more efficient land use and encouraging investment in public amenities and transportation systems.

Consequently, the real estate sector constitutes an indispensable component of the national economy [4,5]. From a macroeconomic standpoint, it exerts a substantial influence on GDP growth [5]. The industry is intricately linked with a wide array of upstream and downstream sectors, forming a complex economic network. Sustainable development within real estate can act as a catalyst for the healthy progression of these related industries [6]. Moreover, housing is widely regarded as one of the most significant forms of collateral and a key consumer good within the economy [7,8], substantially shaping household saving behaviors and consumption decisions [9]. As a result, significant fluctuations in real estate prices can have far-reaching implications not only for the broader economic system but also for social stability [10]. This underscores the critical need to ensure stability within the real estate market.

Real estate policies have gained increasing significance due to their profound influence on housing market dynamics and their broader economic and social implications. Government interventions play a critical role in shaping the trajectory of the real estate sector. In particular, monetary policies—such as interest rate adjustments—directly affect mortgage affordability, thereby influencing home purchasing behavior and investment in real estate markets [11]. Additionally, fiscal policies, including tax incentives and deductions, can enhance the financial attractiveness of homeownership by reducing its overall cost burden, thus encouraging housing demand.

Real estate holds critical importance within the context of China. On one hand, this is due to the sector’s significant role in the country’s economic structure. As of 2023, the real estate industry accounted for approximately 6% of China’s GDP. Moreover, the industry is characterized by an extensive and complex supply chain, encompassing nearly 50 related sectors, including construction, steel, cement, and home appliances. This broad industrial linkage results in a substantial overall contribution to national economic output. On the other hand, the importance of focusing on Chinese market also stems from the dynamic nature of real estate policies. Real estate policy adjustments are closely tied to the broader stages of economic and social development. The evolution of these policies reflects the government’s shifting priorities over time—balancing economic growth, social equity, financial stability, and urbanization. Since 2015, there has been a notable shift in China’s real estate policy objectives: from stimulating growth to curbing speculation and mitigating financial risks. Since then, China’s real estate regulatory approach has become increasingly diversified, incorporating a combination of administrative and market-based instruments. Greater emphasis has also been placed on the establishment of long-term regulatory mechanisms to support the sustainable development of the real estate sector. Meanwhile, the Chinese government has shifted towards more regionally differentiated policy implementation in order to achieve more precise and targeted regulation. Therefore, a thorough analysis of China’s real estate policies is essential for gaining a comprehensive understanding of the sector’s regulatory characteristics, as well as the strategic priorities and policy measures adopted by the government in managing the real estate market.

Previous research has predominantly focused on specific real estate policies and their respective impacts on housing markets. For instance, Shao and White found that imposing aggressive taxation on housing transactions in China can contribute to market stabilization [12]. In contrast, studies suggest that introducing or abolishing transaction taxes in the United States yields relatively limited benefits. Some scholars have examined the pilot implementation of property taxes, noting that while such measures may produce short-term effects, their long-term efficacy appears limited [13]. These studies not only reflect cross-regional differences in policy design and outcomes but also adopt a temporal perspective to assess the evolving impact of real estate regulations over time.

However, previous studies have overlooked the content of policy texts themselves. To address this gap, this study adopts a quantitative approach to systematically analyze Chinese policy documents from 2015–2024 across different cities. By employing a comparative perspective, the study seeks to uncover the heterogeneity of real estate policies across various Chinese cities. Evaluating past policies while taking geographic variation into account can generate valuable insights and practical references for the formulation of real estate policies tailored to different regions and stages of development in the future.

The remainder of this study is organized as follows. Section 2 provides a literature review of real estate and real estate policy. Section 3 outlines the methodology, including data collection and analytical procedures. Section 4 presents the results of the quantitative analysis conducted on policy documents. Section 5 summarizes and discusses the characteristics and differences in real estate policies across different types of cities and proposes potential directions for future research.

2. Literature Review

2.1. Real Estate Markets

Housing constitutes a major component of household wealth and represents one of the most significant categories of household consumption. In the United States, the aggregate value of housing relative to GDP is in the order of 1.3, while housing-related shelter services account for roughly 15% of personal consumption expenditures [14]. In the case of China, the real estate sector has played a pivotal role in national economic development over the past decade. It has accounted for approximately one-sixth of the country’s GDP, one-quarter of total fixed asset investment, 14% of total urban employment, and around 20% of outstanding bank loans. They underscore the industry’s central role as a key engine of China’s economic growth.

Housing prices are influenced by a multitude of factors. On the supply side, key determinants include land prices, developers’ access to financing, and construction costs—with land prices playing a particularly critical role. As one of the fundamental components of housing costs, rising land prices have consistently been identified as a major driver of housing price inflation [15]. On the demand side, variables such as the availability of local amenities, household income levels, educational attainment, and the user cost of housing capital contribute significantly to upward pressure on house prices [16]. Another crucial factor is the expectation of future housing price trends [17,18]. Empirical studies have shown that price expectations, often proxied by past housing price growth, can substantially influence market behavior. For example, Bhatt and Kishor find that such expectations are strongly associated with housing price booms across 18 developed countries [19].

However, the rapid escalation of housing prices has raised widespread concerns about the potential formation of real estate bubbles. Historically, the bursting of highly leveraged housing bubbles has been a primary trigger for numerous financial crises [20]. In situations where the real estate market exhibits signs of dysfunction, government intervention through targeted housing policies can serve as an effective tool to curb excessive price growth and restore market stability [21].

2.2. Classification of Real Estate Policies

Existing research on real estate policy can be broadly categorized into three main areas. The first category focuses on monetary policy. The primary mechanism through which monetary policy affects the real estate market is via interest rate adjustments [22,23], along with changes in money supply, bank lending channels, and mortgage availability. These monetary interventions influence housing prices, thereby motivating a growing body of literature examining their effects on the property market. The second category centers on fiscal instruments, particularly housing-related tax policies. These include transfer taxes, property taxes, land value taxes, as well as mortgage interest tax deductions [14]. The third category pertains to macroprudential regulation, which primarily addresses financial risk and market stability. These policies typically focus on indicators such as the loan-to-income (LTI) ratio, loan-to-value (LTV) ratio, debt-to-income (DTI) ratio, household credit exposure, and down payment requirements. Prior studies have examined the effects of macroprudential measures from both macro-level and micro-level perspectives, including different subgroups such as first-time homebuyers, existing homeowners, buy-to-let investors, and renters [24].

2.3. Influence of Housing Policies in Different Regions on the Real Estate Market

As the level of development in real estate markets varies across countries, the degree of government intervention also differs, resulting in diverse policies. In regions like Europe, South America, and parts of Asia with relatively mature markets, the real estate sector largely operates under free-market principles. In contrast, in several developing Asian countries, real estate markets remain less market-oriented and are characterized by a hybrid model combining state regulation with market mechanisms. In these contexts, government influence plays a decisive role in shaping real estate development trajectories [10]. Previous studies on real estate policy have primarily focused on specific regions, examining how local policies impact their respective housing markets. These studies acknowledge that regional governments often tailor real estate policies to economic, social, and institutional conditions, leading to significant cross-regional variation in policy design and implementation.

China possesses one of the largest real estate markets worldwide. Unlike most countries, where land ownership is typically private or decentralized, Chinese local governments retain ultimate ownership of land and serve as the sole suppliers. They exercise full control over the quantity, composition, and timing of land released for development [25,26]. Furthermore, significant regional disparities exist in terms of economic development and urbanization levels. As a result, the real estate markets in different regions exhibit varying characteristics, and policy priorities differ accordingly. However, the heterogeneity of real estate policies across regions has received limited attention in existing research, representing a notable gap. Particularly for cities at different stages of economic development, variations in developmental goals and strategic positioning give rise to divergent objectives for real estate development. Consequently, the characteristics of their real estate policies also reflect these differences.

This study aims to conduct a comprehensive analysis of China’s real estate policies by adopting a comparative approach to uncover regional variations across different cities. First, it employs quantitative methods to systematically examine the content and characteristics of real estate policies in China. Second, it explores the policy priorities of different regions, taking into account spatial and developmental disparities. This dual perspective contributes to a more nuanced understanding of how real estate policies are formulated and how real estate markets evolve within the context of regional heterogeneity.

3. Methodology and Data

3.1. Topic Models and Evaluation Metrics

3.1.1. LDA

Latent Dirichlet Allocation (LDA) is a widely used probabilistic topic modeling technique for extracting thematic information from large-scale text corpora [27,28]. As effective tools in text mining and natural language processing, topic models uncover hidden thematic structures within document collections. LDA is based on two fundamental assumptions: the document–topic distribution and the topic–word distribution. Specifically, each document is assumed to be a mixture of multiple topics, where each topic is associated with a certain probability of appearing in that document—this forms the document–topic distribution. Similarly, each topic is considered a mixture of multiple words, with each word assigned a probability of occurring within that topic—this constitutes the topic–word distribution. By analyzing the topic–word distribution, one can intuitively grasp the semantic meaning of each topic, thereby supporting tasks such as text classification, clustering, and content recommendation.

3.1.2. NML

Nonnegative Matrix Factorization (NMF) is a linear algebraic model that decomposes high-dimensional vectors into low dimensional representations, which can generate easily interpretable text data clusters, especially for short texts [29]. The NMF principle is to transform the topic recognition problem into a constrained optimization problem to solve, by decomposing a term–document matrix (A) into the product of a terms–topics matrix (W) and topics–documents matrix (H) [29]. Like LDA, NMF is also widely used in topic modeling across various social science research [30]. Therefore, this article incorporates it into the comparative framework to alleviate uncertainty in the modeling process.

3.1.3. Evaluation Metrics

Due to the subjective nature of comparing and analyzing topic clustering algorithms based on the clustering results of the dataset [30], it is impossible to objectively measure the effectiveness of topic clustering. Therefore, we mainly used two objective indicators to evaluate the topic modeling effectiveness of the two models, namely Topic diversity (TD) and Topic consistency (TC).

The calculation process of TD is relatively simple. This indicator is obtained by calculating the proportion of non-recurring topic words in all topics in the topic modeling results. The smaller the value of TD, the higher the redundancy of topic information identified by the model; The larger the value of TD, the more diverse themes the model has identified [31]. The calculation method of thematic diversity (TD) is shown in formula (1):

$$\mathrm{T}\mathrm{D}={\displaystyle \frac{\{{\mathrm{T}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{c}}_1^{\mathrm{n}},{\mathrm{T}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{c}}_2^{\mathrm{n}},\dots ,{\mathrm{T}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{c}}_{\mathrm{k}}^{\mathrm{n}}\}}{{\sum }_{\mathrm{i}=1}^{\mathrm{k}}{\mathrm{T}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{c}}_{\mathrm{i}}^{\mathrm{n}}}}$$

(1)

where ${\mathrm{T}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{c}}_{\mathrm{i}}^{\mathrm{n}}$ represents the first $\mathrm{n}$ topic words of the topic $\mathrm{i}$, $\{{\mathrm{T}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{c}}_1^{\mathrm{n}},{\mathrm{T}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{c}}_2^{\mathrm{n}},\dots ,{\mathrm{T}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{c}}_{\mathrm{k}}^{\mathrm{n}}\}$ represents the set of non-repeating topic words, ${\sum }_{\mathrm{i}=1}^{\mathrm{k}}{\mathrm{T}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{c}}_{\mathrm{i}}^{\mathrm{n}}$ denotes the total number of topic words. This indicator is used to measure the richness of document information covered by topic words after topic modeling.

TC is mainly used to evaluate the topic coherence of topic models, calculated using Normalized Pointwise Mutual Information (NPMI), with the following formula (2) [32]:

$$\mathrm{T}\mathrm{C}={\displaystyle \frac{1}{\left|\mathrm{T}\right|}}{\sum }_{\mathrm{t}=1}^{\left|\mathrm{T}\right|}{\displaystyle \frac{2}{\mathrm{N}(\mathrm{N}-1)}}{\sum }_{\mathrm{i}=1}^{\mathrm{N}-1}{\sum }_{\mathrm{j}=\mathrm{i}+1}^{\mathrm{N}}\mathrm{N}\mathrm{P}\mathrm{M}\mathrm{I}({\mathrm{w}}_{\mathrm{i}},{\mathrm{w}}_{\mathrm{j}})$$

(2)

where |T| represents the number of topics, $\mathrm{N}$ represents the number of words contained in the topic, $\mathrm{N}\mathrm{P}\mathrm{M}\mathrm{I}({\mathrm{w}}_{\mathrm{i}},{\mathrm{w}}_{\mathrm{j}})$ represents the normalized index of the co-occurrence frequency of words ${\mathrm{w}}_{\mathrm{i}}$ and ${\mathrm{w}}_{\mathrm{j}}$ within a certain window in the document set.

3.2. Keyword Frequency and Co-Occurrence Analysis

Keyword frequency analysis serves as an effective method for swiftly identifying and discussing core knowledge or hot spots within extensive text data [33], making it a widely utilized tool in text analysis. Additionally, keyword co-occurrence networks are frequently employed in the development of knowledge maps during quantitative research to elucidate the knowledge structure within specific fields [34]. In comparison to keyword frequency analysis, this approach can more effectively investigate the relationships between keywords across various documents. Consequently, this study performed both keyword frequency analysis and keyword co-occurrence analysis on real estate policy texts from multiple cities, aiming to provide a more comprehensive understanding of the similarities and differences among these texts. Specifically, the size of node $\mathrm{i}$ represents their weighted degree centrality, as shown in the formula (3):

$${\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{d}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}}_{\mathrm{i}}={\sum }_{\mathrm{j}=1}^{\mathrm{N}}{\mathrm{w}}_{\mathrm{i}\mathrm{j}}$$

(3)

where $\mathrm{N}$ is the total number of nodes, ${\mathrm{w}}_{\mathrm{i}\mathrm{j}}$ is the weight between node $\mathrm{i}$ and node $\mathrm{j}$, usually representing the show the 150 high-frequency words as network edges and the edges with ${\mathrm{w}}_{\mathrm{i}\mathrm{j}}>5$.

3.3. Research Framework

The policy knowledge graph constructed in this study consists of two main components: a knowledge graph derived from academic literature and another based on municipal policy texts. The former facilitates an understanding of relevant knowledge within the academic community, while the latter provides industry stakeholders with insights into the real estate market conditions across different cities. Figure 1 illustrates the process of constructing and analyzing the city-level policy text knowledge graph.

3.4. Data Collection and Processing

The data collection for this study primarily focuses on policy documents issued over the past decade (2015–2024) related to the keywords “real estate”, “construction industry”, and “commercial housing”. To ensure data accuracy and reliability, relevant normative documents were obtained from the official websites of municipal Housing and Urban-Rural Development Bureaus across various cities. In selecting and categorizing cities, we primarily referred to the Ranking of Cities’ Attractiveness in China released annually by YiMagazine (It is China’s first weekly business news magazine and is published under Yicai Media Group, the country’s largest financial news conglomerate) since 2016 [35]. This ranking evaluates cities across five dimensions: concentration of commercial resources, connectivity as urban hubs, population vitality, competitiveness in the new economy, and future growth potential. These dimensions are critical for assessing urban development prospects, attractiveness, and economic dynamism. Building on an analysis of nearly a decade of ranking data, and supplemented by indicators such as GDP scale, GDP growth rate, and population size, we compiled a list of economically dynamic cities and subsequently collected their real estate policy documents for analysis. The details of the collected data are presented in

Table 1. Data on policy texts by city. (All documents are accessed on 16 January 2025).

No.	City	Documents	Words	Source	Types of cities
1	Beijing	38	28,724	https://zjw.beijing.gov.cn/	First-tier Cities
2	Shanghai	31	30,019	https://zjw.sh.gov.cn/
3	Guangzhou	35	45,155	https://zfcj.gz.gov.cn/
4	Shenzhen	34	42,474	https://zjj.sz.gov.cn/
5	Chengdu	11	15,701	https://cdzj.chengdu.gov.cn/	New first-tier cities
6	Hangzhou	12	7955	https://fgj.hangzhou.gov.cn/
7	Nanjing	14	13,196	https://fcj.nanjing.gov.cn/
8	Wuhan	6	7190	https://zgj.wuhan.gov.cn/
9	Chongqing	11	16,525	https://zfcxjw.cq.gov.cn/
10	Suzhou	10	10,205	https://zfcjj.suzhou.gov.cn/
11	Tianjin	10	16,781	https://zfcxjs.tj.gov.cn/	Cities with strong economy
12	Qingdao	6	8680	https://sjw.qingdao.gov.cn/
13	Zhengzhou	42	27,082	https://zfbzj.zhengzhou.gov.cn/; https://zzjsj.zhengzhou.gov.cn/
14	Hefei	40	29,905	https://cxjsj.hefei.gov.cn/; https://zfbzfcglj.hefei.gov.cn/
15	Xiamen	26	26,652	https://szjj.xm.gov.cn/
16	Foshan	42	45,276	https://fszj.foshan.gov.cn/	Cities with economic development potential
17	Wuxi	19	19,010	https://js.wuxi.gov.cn/
18	Nanchang	27	16,897	https://zjj.nc.gov.cn/
19	Shijiazhuang	16	14,315	https://zjj.sjz.gov.cn
20	Haikou	18	21,811	http://hkjsj.haikou.gov.cn/
21	Tangshan	11	12,004	https://zhujianju.tangshan.gov.cn/

.

Following data acquisition, an initial data cleaning process was conducted to remove irrelevant content, duplicate records, and formatting errors. During the text preprocessing stage, Jieba 0.42.1 in Python 3.8 was employed to perform Chinese words segmentation on the collected textual data, thereby laying the groundwork for subsequent analysis. In addition, as it is a policy document involving some indivisible proprietary noun phrases, a list of custom words and stop words was constructed as shown in Supplementary Materials to improve the quality of word segmentation.

4. Results

To investigate the real estate regulation in China’s most economically vibrant cities, this study analyzes policies from 21 representative urban cases and develops a knowledge graph of their real estate policy frameworks. While these cities all demonstrate strong economic vitality, differences in their developmental stages and levels of economic growth have resulted in distinct policy orientations. Understanding this heterogeneity not only highlights the varying regulatory priorities across cities but also provides critical insights into how urban development trajectories shape policy design in China’s real estate sector.

4.1. Mapping the Real Estate Policy Knowledge in China

4.1.1. Analysis of High-Frequency Keywords

We construct a knowledge graph of China’s real estate policy landscape by performing a high-frequency keyword analysis of policy documents from 21 economically dynamic cities. This corpus-driven approach provides a comprehensive view of the primary governance priorities in regions with strong economic momentum. Following standard preprocessing and word segmentation, we extracted 426,832 keyword instances. For each city, the 150 most frequent terms were retained as the high-frequency set. Appendix A reports, by city type, the top ten high-frequency keywords for each city.

We classified the core keywords based on their contextual usage within the policy texts.

Table 2. The principal governance dimensions of real estate policy across economically dynamic regions.

Themes	Keywords	Representative Cities
Governance	Regulation; Supervision; Monitoring; Inspection; Standard	All types
Construction and planning strategy	Construction; Architecture; Urban and rural construction; Housing security	Economically strong cities and high-potential growth cities
Financial and fiscal instruments	Subsidy; Fund; Loan; Bank; Housing provident fund	All types
Rental market	Leasing; Rental	All types
Talent strategies	Residence; Employee; Talent; Talent apartment	Economically strong cities and high-potential growth cities

synthesizes the principal governance dimensions of real estate policy across economically dynamic regions.

Governance is a cross-cutting theme that permeates China’s real estate policy corpus. It aims to preserve market order and provides guidance across the full industry value chain—from real estate construction and housing transactions to rental markets and property management services. These measures also delineate departmental responsibilities and emphasize inter- department coordination. As shown in Appendix A, regulation is an indispensable policy orientation across all city types, thereby setting the baseline tone for China’s real estate governance.

Real estate construction and planning constitute another major policy focus, encompassing topics such as “construction,” “architecture,” and “housing security.” Conceptually, these measures operate on the supply side. In practice, policy instruments include optimizing urban spatial structure and land use patterns and rationally controlling the release of new land for real estate, advancing urban renewal and the rehabilitation of aging residential communities and coordinating the redevelopment of underutilized or low-efficiency land. Over the long term, construction and planning policies drive urban spatial restructuring and the upgrading of housing quality. Across city types, references to construction and planning appear more frequently in economically strong cities and high-potential growth cities, consistent with their need to accelerate new-type urbanization and sustain rapid economic expansion.

Financial and fiscal instruments are a core pillar of real estate policy. Implemented as a coordinated policy mix—encompassing interest-rate reductions, expanded lending, tax relief and other instruments—these measures promote the sector’s orderly and steady development. On the demand side, they lower households’ effective cost of home purchase. On the supply side, they standardize project management and ease developers’ financing constraints by improving the availability and terms of credit.

Rental market emerges as a central policy focus. Leasing policies are designed to regulate housing rental activities, safeguard the lawful rights and interests of contracting parties, and stabilize landlord–tenant relations. The high-quality development of the rental market plays a vital role in improving urban housing conditions and advancing new-type urbanization. Notably, rental-related governance is a shared priority across city types—ranging from highly developed first-tier cities to fast-growing, high-potential cities.

From a goal-oriented perspective, real estate policy in China seeks not only to stabilize market performance but also to align with local talent strategies. Reflecting the heterogeneous development paths and growth needs across city types, many policies deploy housing and family-support programs to optimize public service provision and the allocation of public resources, thereby improving talent attraction and employment matching and, in turn, strengthening urban growth dynamism. Notably, in economically advanced and high-potential cities, leveraging real estate policy to attract and retain talents and to support population growth has become both a key instrument for stabilizing housing markets and an emerging policy trend.

Overall, first-tier cities, having passed through a phase of rapid economic expansion, now prioritize the sustainability of urban development. Their real estate policies are therefore broader in scope, encompassing multiple dimensions of regulation and governance, and are oriented toward establishing clear boundaries for market development. By contrast, in rapidly growing cities—particularly economically strong cities and those with high development potential—real estate policies place greater emphasis on planning and expansion. At this stage, such policies aim to advance new-type urbanization and leverage housing-related measures to attract talent, thereby serving the pressing needs of accelerated urban growth.

4.1.2. Keywords Co-Occurrence Networks

Compared with high-frequency keyword analysis, co-occurrence networks can offer a more nuanced understanding of the strength of relationships between terms. To investigate the core policy concerns reflected in policy documents, this study constructed keywords co-occurrence networks for different categories of cities based on the top 150 most frequent terms in each city. The results are illustrated in Figure 2, Figure 3, Figure 4 and Figure 5.

Based on the keyword co-occurrence networks derived from the real estate policy texts of China’s first-tier cities (Figure 2), it is evident that these cities consistently prioritize the timely governance and regulation of housing and the construction sector, with particular emphasis on terms such as “timely”, “regulation”, “management”, “construction”, “standard” and “quality”. Specifically, in Beijing, policy documents frequently call for the Housing and Urban-Rural Development Commission and other relevant competent authorities to focus on “formulate”, “implementation”, and “standardize” of related activities. In Shanghai, attention is directed toward “housing”, “quality”, “validity period”, and “be responsible for”, In Guangzhou, keywords such as “housing”, “leasing”, “in accordance with the law”, and “property rights” frequently co-occur, reflecting the city’s policy focus. Shenzhen’s policy texts, meanwhile, prominently feature terms such as “housing”, “fund”, “requirement” and “leasing”, indicating a concentration on the management of housing through financial and leasing mechanisms.

The keyword co-occurrence networks for real estate policy texts in China’s new first-tier cities (Figure 3) indicate that these cities primarily focus on real estate development, rental markets, and housing security. Specifically, Chengdu emphasizes the “management” and “regulation” of “real estate”, along with the “responsibility” of related department. Hangzhou pays particular attention to the regulation of the “housing” and “leasing” markets, covering aspects such as “fund”, “reward and subsidy”, and “housing supply”. Nanjing focuses on “housing security”, “house”, and “rental,” while also highlighting administrative reform through the coexistence of keywords such as “services”, “enterprise”, and “regulation”. In Wuhan, policy documents frequently mention the management of “notice”, “information”, and “behavior”, as well as the registration and regulation of “leasing” and related “contracts”. Chongqing places emphasis on “fund”, “approval” “notice” and “content” for real estate projects, while Suzhou is concerned with the “security” of “housing”.

In China’s economically strong cities, policy priorities are closely tied to the development, construction, and management of commercial housing (Figure 4). In Tianjin, frequent co-occurrences of terms like “new construction” “commercial housing” “real estate” and “contracts” in policy texts highlight the city’s focus on contract formulation and management for newly built commercial properties. This suggests that enterprises should pay close attention to project development, strengthen management and filing procedures, and ensure regulatory compliance. In Qingdao, the co-occurrence network is relatively sparse, with frequent references to “housing” “existing stock” “presales” and “management” indicating an emphasis on sales performance and the provision of policy support for commercial housing transactions. Zhengzhou exhibits a denser co-occurrence network, with terms such as “further” “strengthen” “service” “regulation” and “management” reflecting the city’s growing focus on housing market governance; enterprises are thus advised to safeguard their reputation and creditworthiness throughout the development and bidding process. Hefei shows a pattern similar to Zhengzhou, also gradually intensifying its management of the real estate market. In contrast, Xiamen places particular emphasis on the “regulation” “evaluation” and “management” of construction projects.

China’s cities with strong economic development potential represent important emerging markets for the real estate industry. These cities generally focus on housing development as well as enterprise management and service delivery (Figure 5). In Foshan, frequently co-occurring keywords include “strengthen”, “real estate”, “housing”, “leasing”, “regulation”, and “management”, suggesting a strong policy emphasis on oversight and governance. In Wuxi, keywords such as “real estate”, “management”, “optimize”, “standardize”, “enterprise” and “market” frequently co-occur, indicating that firms should stay informed about newly issued or repealed policy documents to ensure regulatory compliance. In Nanchang, the focus is on the development activities of real estate enterprises as well as the processes of housing rental, inspection, and sales. Shijiazhuang’s co-occurrence network centers on terms such as “fund”, “transactions,” “management”, and “regulation”, highlighting the importance of financial oversight. In Haikou, the policy emphasis is primarily on “housing”, “contract” and “filing”, while in Tangshan, terms like “development zone,” “market”, “real estate”, and “loan” frequently appear, suggesting that local policies are guiding capital flow and financing within the housing market support.

The keyword co-occurrence networks provide deeper insights into how cities at different stages of economic development shape their real estate policies around distinctive priorities. In first-tier cities, policies emphasize high-quality and sustainable industrial development, with a strong focus on regulatory measures, implementation standards, and evaluation mechanisms. In new first-tier and economically strong cities, policy frameworks reflect an effort to balance development and regulation simultaneously. In cities with high development potential, current policies place greater weight on expanding the real estate sector and leveraging it as a role to drive economic growth.

4.2. LDA Topic Analysis

Section 4.1 employs keyword analysis to construct a knowledge graph of real estate policies from 21 economically dynamic Chinese cities. This approach provides a direct visualization of the primary policy priorities in these cities and facilitates comparative analysis of differences across city types, as well as the associations among key terms. Section 4.2 applies LDA to thematically classify the policy documents, thereby uncovering the latent topic structures of real estate policies and enabling a comparative assessment of thematic characteristics across different categories of cities.

Firstly, we compared the performance of LDA and NMF on the overall data based on the two metrics in Section 3.1.3, as shown in Figure 6. NMF is implemented through Sk-learn 1.3.2, LDA is implemented through Gensim 4.1.2, and the programming language environment is Python 3.8. Moreover, to ensure the reproducibility of the results, the NMF parameters are uniformly set to {random_state = 42, l1_ratio = 0.5}, and all other parameters are set to default values; The LDA model parameters are uniformly set to {chunksize = 1000; passes = 5, random_state = 42, update_every = 1}, and all other parameters are set to default values. In this study, the unified setting of model parameters runs through the entire research.

In terms of topic diversity, LDA performs better than NMF, indicating that the LDA model recognizes topic information with less redundancy; In terms of topic consistency, LDA only outperforms NMF when the number of topics is 2. In this context, considering that the research object is policy documents with long texts and the advantages of LDA over NMF in analyzing long texts [36], we hope to use LDA to mine richer policy semantic information and provide more comprehensive insights for interpreting China’s real estate policies. Therefore, by examining the keywords and their weights within each city’s topics, we identify policy priorities and regional variations, providing insights into the real estate policy environment and future trends. The optimal number of topics for each city was determined through coherence testing [28,37], as shown in Figure 7.

4.2.1. Topic Analysis of First-Tier Cities

The LDA topic modeling results are as shown in Figure 8, Figure 9, Figure 10 and Figure 11. Figure 8 presents the results of the LDA-based topic analysis of real estate policies in the four first-tier cities.

As the capital of China, Beijing possesses extensive political, cultural, technological, and educational resources. The thematic structure of its policy documents primarily revolves around two topics. In Topic 1, the term “housing” is assigned a weight of 0.036, while “subsidy” carries a weight of 0.017. Keywords such as “house”, “family”, and “leasing” collectively underscore the development of a housing security system, particularly addressing the satisfaction of public needs for housing supply and family accommodation requirements. This includes policies related to housing support and subsidies. In Topic 2, terms like “construction” (0.020), “project” (0.019), and “standard” (0.019) highlight the regulatory framework governing real estate development and construction processes—from project approval to construction standards—emphasizing the necessity for coordination between project quality and urban infrastructure to fulfill high-standard demands for housing construction in the capital city. These analyses indicate that Beijing’s policies are designed to safeguard public welfare by enhancing both the housing supply and security systems while simultaneously improving construction quality through standardized practices that promote livability and sustainable urban development.

Shanghai serves as a major economic hub and an important center for international finance and trade. The thematic focus of its policy documents primarily falls into the following four categories. In Topic 1, the term “quality” holds a weight of 0.024, with “management” at 0.020; keywords such as “acceptance”, “residence”, and “engineering” work synergistically to emphasize the establishment of a comprehensive quality control system throughout all phases of real estate development. This focus encompasses management norms from engineering construction through acceptance procedures, ensuring both housing quality and residents’ living experiences align with high-quality expectations characteristic of megacities while establishing fundamental safeguards for public livelihood in terms of housing provision. In Topic 2, key terms including “assessment” (0.035), “housing” (0.033), and “house” (0.033) refer to regulations governing property acquisition and evaluation within urban renewal contexts—managing an entire chain from application processes through assessment stages—to ensure equitable acquisition practices that facilitate organic urban renewal efforts alongside spatial resource optimization. In Topic 3, the terms “filing” (0.050) and “leasing” (0.050) are linked to the filing and assessment mechanisms of the rental market, contributing to the establishment of an orderly rental ecology that responds to the demand for rental housing driven by talent agglomeration. This also promotes the implementation of policies aimed at achieving simultaneous development in both renting and purchasing. In Topic 4, keywords like “housing” (0.032) and “property rights” (0.024) emphasize the coordination between housing property rights management and living security. The discussion explores innovative models like shared property rights, enhances security related to property rights and living conditions, adapts to diverse housing needs, and aids in constructing a multi-level housing security system. These analyses indicate that Shanghai’s policies align with its positioning as an international economic center through quality control measures, acquisition norms, innovations in leasing practices, and coordinated property rights management—thereby supporting efforts to build a distinguished global city characterized by refined governance that safeguards livelihoods, fosters urban renewal initiatives, and invigorates the housing market.

Guangzhou, as the economic center of south China, is characterized by highly active commercial and trade activities. The policy documents of the city primarily focus on the following two topics. In Topic 1, keywords such as “leasing” (0.023), “property rights” (0.017), and “project” (0.017) collectively focus on norms governing the rental market and property rights transactions. While promoting simultaneous development in both renting and purchasing sectors, these elements strengthen coordination between property transaction processes and project development activities to meet the diverse demands of the Greater Bay Area’s housing market effectively. In Topic 2, “leasing” emerges prominently with a weight of 0.033; alongside this are keywords like “public” (0.028) and “application” (0.021), which together underscore efforts toward establishing a public rental housing system—from application procedures through public resource investment—to ensure adequate supply for livelihood-oriented rental properties while addressing talent accommodation needs within the Greater Bay Area context. The core orientation is thus defined: leveraging both rental markets and established property right norms as foundational supports tailored to accommodate varied requirements across the Greater Bay Area, taking public rental housing as the foundation to ensure people’s livelihood and talent housing, and driving the real estate market to adapt to local development strategies.

Shenzhen, known for its high-tech industry and innovation-driven growth, has experienced rapid economic development. The city’s policy documents focus on four key areas. In Topic 1, keywords such as “project” (0.049), “security” (0.030), and “leasing” (0.021) highlight the coordinated development of real estate projects and rental housing to meet the diverse living needs of talent in a science and technology innovation city. In Topic 2, “housing provident fund” (0.080) is emphasized, with keywords like “employee” (0.034) and “withdraw” (0.034) focusing on facilitating provident fund withdrawals and usage to stimulate housing consumption and support talent housing. In Topic 3, keywords such as “house” (0.041), “appraisal” (0.032), and “safety” (0.030) underscore the supervision of housing quality and safety to ensure the living safety of talent. In Topic 4, keywords such as “leasing” (0.042) and “applicant” (0.013) focus on refining the management of the rental market, with close attention paid to the rental application process and housing supply links. This aims to continuously improve the ecosystem of simultaneous development of renting and purchasing, thereby providing robust support for the living needs of talent. The overall orientation is to coordinate project development and leasing to meet talent needs, activate housing consumption through the provident fund, ensure living quality through supervision, and build a real estate ecosystem suitable for a science and technology innovation city.

The policies of first-tier cities all cover the whole process of real estate development and construction, market transaction, leasing and security. For example, Beijing’s “construction” and Guangzhou’s “project” reflect the concern for development and construction. Guangzhou’s “property rights” and Shenzhen’s “transaction” are related to transaction management, and the four cities all have high-frequency “leasing”, constructing a complete policy chain. Meanwhile, they all attach importance to market supervision and financial risk prevention and control. Keywords such as “supervision” and “fund” have high weights in local policies, indicating that each first-tier city ensures the stability of the real estate market by strictly controlling funds and standardizing enterprises, adapting to the positioning of the economic core engine. The difference is that each city has its own focus based on its own functional positioning: Beijing focuses on people’s livelihood support and construction norms, Shanghai focuses on international financial and high-end market adaptation, Guangzhou strengthens the diversified needs of the Greater Bay Area and public leasing, and Shenzhen highlights the adaptation of science and technology innovation talent and the deepening of the provident fund.

4.2.2. Topic Analysis of New First-Tier Cities

Figure 9 presents the results in the six new first-tier cities.

Figure 9. Topic modeling results for new first-tier cities’ policies.

Figure 9. Topic modeling results for new first-tier cities’ policies.

Suzhou’s real estate policy mainly focuses on five topics. Topic 1 encompasses structural issues, risk management, urban development, housing sales, and market dynamics, reflecting a focus on the comprehensive regulation of the real estate market. Topic 2 shows that policy texts may discuss financial and human resource management issues related to real estate, such as insurance business, order processing, loan services, employee benefits, and fund withdrawal. Topic 3 indicates that policy texts may involve relevant issues of the construction industry, real estate evaluation, system management, project investment, and investment companies. Topic 4 discusses sales strategies, price control, rental market, and project development. Topic 5 covers construction technology, specifications, advanced construction methods, and project requests. Overall, the analysis of Suzhou’s real estate market policy texts reveals the diversity of policy focuses, from financial and human resource management to construction industry and market leasing, as well as technical specifications and project development. These topics reflect the comprehensiveness and integrity of Suzhou’s real estate market policies.

In Hangzhou, Topic 1 centers on deepening housing security, with keywords such as “security” (0.047), “application” (0.037), and “family” (0.033) highlighting the construction of a precise housing security system tailored to the needs of talents in the digital economy. Topic 2 focuses on the rental market, with “leasing” (0.047) as a core keyword, emphasizing financial support for rental enterprises to optimize the housing ecology for digital economy talent. Hangzhou’s policies thus aim to meet the needs of talent with precise housing security, and relies on the coordinated development of the rental market, finance, and enterprises to build a livable ecology, providing strong support for the construction of a digital economy highland and the improvement of urban quality.

Chengdu’s policies in Topic 1 emphasize the whole process management of real estate projects, with “engineering” (0.058), “project” (0.040), “construction” (0.019), and “investment” (0.017) as key terms, ensuring compliance and efficiency in large scale urban development. Topic 2 highlights housing finance, with “housing provident fund” (0.032), “credit” (0.031), and “loan” (0.026) become the core keywords, aiming to stimulate housing consumption and meet residential needs. Topic 3 underscores the integration of the rental market with the provident fund system, with “housing provident fund” (0.033), “leasing” (0.031) and “center” (0.023) as key terms, promoting a balanced housing system that supports talent housing and urban living structure optimization. Overall, Chengdu’s real estate policies ensure the quality and efficiency of development with the whole process management of projects, activate market vitality with housing financial policies, and build a coordinated system of renting and purchasing, so as to fully promote the construction of a livable and business-friendly western central city.

Nanjing’s Topic 1 prioritizes market supervision, with “supervision” (0.078), “fund” (0.047), and “transaction” (0.029) as key terms, aiming to prevent market risks, ensure transaction fairness, and lay a market foundation for the stable development of historical and cultural cities. Topic 2 links talent housing with the rental market, emphasizing rental housing as a key support for talent attraction in an innovative city, such as “leasing” (0.041), “housing” (0.028), and “talent” (0.016) are interrelated. Topic 3 focuses on property rights management and housing security, balancing historical preservation with modern living needs, with terms like “property rights” (0.041), “housing” (0.036), and “security” (0.031). Nanjing’s policies thus ensure market stability through supervision, attract talent through rental support, and balance historical and modern needs through property rights norms and affordable housing.

Wuhan’s policies in Topic 1 focus on “leasing” and “registration” to standardize leasing and property rights registration and build an orderly market. Topic 2 emphasizes strict fund control and enterprise behavior regulation to stabilize the real estate market, with terms like “supervision”, with “fund” and “enterprise”. Topic 3 deepens the supervision of the rental market and is associated with fund account management to ensure the standardized operation of the rental market. Overall, it takes leasing and property rights norms as the basis, and through strict supervision and coordination with funds and enterprises, it supports industrial development and talent housing and promotes the steady development of the regional real estate market.

Chongqing’s policies cover multiple aspects: Topic 1 focuses on expanding housing supply through rental market coordination. Topic 2 strengthens market supervision to ensure housing and rental norms. Topic 3 strengthens project and enterprise supervision to ensure development quality and efficiency. Topic 4 strengthens housing engineering safety and design specifications for the terrain of mountain cities. Topic 5 builds a credit and information system to standardize market order. Topic 6 uses subsidy policies to drive housing supply. The policy takes leasing and public housing as the support, supervision and norms as the cornerstone, and credit and subsidies as the driving force to promote the real estate market to adapt to the goals of urban and rural development and livable construction.

In summary, new first-tier cities generally prioritize livelihood-related topics, such as “family”, “security”, and “housing”, focusing on basic housing rights and using affordable housing and provident fund policies to achieve “housing for all”. Meanwhile, these cities also emphasize the synergy between real estate and urban development, combine “project”, “development” and other keywords with urban planning and industrial supporting, and use real estate to promote urban function upgrading and narrow the development gap with first-tier cities. However, there are notable differences between cities. Inland cities like Chengdu and Wuhan focus more on the activation of local people’s livelihood and the adaptation to regional strategies, such as Chengdu’s provident fund policy and Wuhan’s leasing policy. While cities in the Yangtze River Delta such as Hangzhou and Nanjing pay more attention to innovative supply and the coordinated development of characteristic industries, such as Hangzhou’s shared property rights and Nanjing’s property rights norms, as well as the digital economy and historical and cultural industries. Suzhou and Chongqing, according to their own urban positioning, either strengthen the precise regulation of local policies or adapt to the diversified needs of mountain cities/municipalities directly under the Central Government, resulting in the differentiation of policy paths. These differences reflect that each city has adopted different policy paths based on different regions, industries, and urban foundations.

4.2.3. Topic Analysis of Cities with Strong Economy

Figure 10 illustrates the outcomes of the LDA-based topic analysis of real estate policies in China’s five cities with strong economy.

Figure 10. Topic modeling results for policies from cities with strong economy.

Figure 10. Topic modeling results for policies from cities with strong economy.

In Tianjin, Topic 1 focuses on the supervision and whole-chain management of the real estate market, in which “supervision” (0.063) has the highest weight, and works together with “fund” (0.033), “commercial housing” (0.031), “enterprise” (0.026), and “real estate” (0.025) to standardize market order and ensure the compliance of development and transaction. Topic 2 highlights the coordination of housing leasing with public and family housing needs, and emphasizes the construction of a leasing and affordable housing system to meet urban housing needs. Tianjin’s core orientation is to standardize development and transaction through strict market supervision, cover diversified housing needs with a leasing and affordable housing system, promote the steady development of the real estate market, and adapt to urban development.

Zhengzhou’s policies in Topic 1 focus on the whole process of housing supply, leasing, safety, and review, in which “housing” (0.037) has the highest weight, and works together with keywords such as “leasing” (0.022), “security” (0.015), “examination” (0.014), and “project” (0.012) to ensure the basic supply of housing and living safety. Topic 2 focuses on engineering bidding, enterprise participation, and salary correlation, standardizes the market order of the construction link, and ensures the quality and efficiency of development. Topic 3 focuses on construction enterprises, transactions, and engineering construction, and promotes the coordination of construction industry norms and real estate development. Zhengzhou’s core orientation is to take the whole process guarantee of housing as the basis, standardize the market order of the construction link, and link the construction industry to meet the needs of real estate development in central hub cities.

In Xiamen, Topic 1 focuses on project management, emphasizing “project” (0.030), “evaluation” (0.026), and “proxy construction” (0.015) to standardize development processes and ensure quality. Topic 2 highlights “bidding” (0.016), “architecture” (0.015), and “waste” (0.013), regulating tendering and resource use. Together, they form a lifecycle management system—from development to evaluation—optimizing construction compliance and efficiency in this coastal tourist city while maintaining sustainable resource allocation.

In Hefei, Topic 1 emphasizes housing project review, approval, and construction norms. “Examination” (0.020) has the highest weight, working with “approval” (0.014), “architecture” (0.013), “housing” (0.012), and “engineering” (0.012) to ensure development compliance and housing quality. Topic 2 focuses on construction enterprise credit, information management, and project development, building a credit system to standardize the process. Topic 3 centers on real-estate fund supervision, the rental market, and housing supply, ensuring fund safety and meeting rental and housing needs. Hefei aims to ensure development compliance via review, standardize the market with the enterprise credit system, and coordinate fund supervision and leasing to fit the needs of technological innovation and regional central city construction.

Qingdao’s real estate governance framework strategically integrates multiple priority areas, with housing safety emerging as the paramount concern as evidenced by the dominant weight of “safety” (0.104). This safety-focused approach is systematically reinforced through coordinated emphasis on proper building “use” (0.066), comprehensive “management” (0.018), strong governmental oversight (“People’s Government” 0.017), and robust legal frameworks (“law and regulations” 0.015). The city simultaneously addresses its coastal open-city characteristics through a talent-centric development model, where “talent” (0.053) attraction is carefully balanced with optimized “land use” (0.032) and a well-regulated “leasing” (0.020) market. Market operations are further refined through meticulous attention to contractual agreements (“agreement” 0.002), permitting processes (“permit” 0.002), and behavioral standards (“behavior” 0.002), while resource allocation strategies effectively connect housing availability (“leasing” 0.029, “unit” 0.020) with talent needs. A sophisticated supervision system (“supervision” 0.087) ensures market stability through rigorous monitoring of leasing activities, financial security (“fund” 0.046), and commercial housing management, collectively forming a comprehensive approach that supports Qingdao’s development as a model coastal livable city. Qingdao’s housing policy framework combines property rights management, talent housing solutions, and financial oversight to ensure transaction legality, attract skilled professionals, and safeguard market stability. This integrated approach balances affordable housing transparency with robust consumer protection while supporting sustainable urban development.

Our study reveals that these five economically developed cities have established comprehensive real estate policy systems encompassing development, transactions, and leasing, all with robust regulatory mechanisms. While maintaining fundamental safeguards, each city has developed distinctive policy adaptations: Tianjin prioritizes leasing security to facilitate regional coordination; Zhengzhou aligns construction standards with industrial development requirements; Hefei implements specialized financial oversight to foster innovation; Qingdao integrates safety regulations with talent housing initiatives; and Xiamen tailors its project management approach to support tourism development. This dual approach of maintaining core protections while implementing context-specific adjustments demonstrates how cities effectively customize their governance frameworks to address unique developmental challenges.

4.2.4. Topic Analysis of Cities with Economic Development Potential

Figure 11 displays the LDA-based topic analysis of real estate policy documents from the six cities with economic development potential.

Figure 11. Topic modeling results for policies from cities with economic development potential.

Figure 11. Topic modeling results for policies from cities with economic development potential.

The policy framework of Foshan can be broadly categorized into five topics. In Topic 1, “real estate” (0.060) leads in weight, and “information” (0.027) and “brokerage” (0.026) work together to focus on real estate information and brokerage services and standardize the basic ecology of market transactions. In Topic 2, “enterprise” (0.037) and “competent authority” (0.020) focus on the coordination between enterprises and competent departments and strengthen the supervision of market entities. In Topic 3, “examination” (0.025) and “engineering” (0.014) work together to focus on engineering review and construction management to ensure development compliance. In Topic 4, “project” (0.026) and “housing” (0.022) are linked to associate project development with housing supply to adapt to the housing needs of industrial populations. In Topic 5, “housing” (0.067) and “leasing” (0.050) have high weights, focusing on the housing leasing market to adapt to the housing needs of talent in large manufacturing cities. To sum up, Foshan takes the standardization of transaction ecology as the foundation, strengthens enterprise supervision and engineering compliance, links project development with the leasing market, adapts to the housing supply and demand structure of the Pearl River Delta manufacturing strong city, and ensures the housing of industrial populations.

The policy documents of Nanchang reflect three major thematic priorities. In Topic 1, “inspection” (0.031) and “green” (0.024) work together to focus on engineering inspection and green buildings to ensure development quality and ecological adaptation. In Topic 2, “supervision” (0.054) and “fund” (0.039) cooperate to strengthen fund supervision and market supervision and standardize the development process. In Topic 3, “housing” (0.022) and “safety” (0.014) are linked to focus on housing safety and living security and guarantee people’s livelihood needs. To sum up, Nanchang takes engineering quality and green buildings as the foundation, strengthens fund supervision and market supervision, guarantees housing safety and people’s livelihood needs, and adapts to the real estate stable development and people’s livelihood guarantee function of the capital city of the Yangtze River Middle Reaches urban agglomeration.

The policy documents of Shijiazhuang highlight three major thematic areas. In Topic 1, “public housing” (0.029) and “sell” (0.027) work together to focus on the supply of public rental housing and the sales of commercial housing to balance the needs of security and the market. In Topic 2, “supervision” (0.085) and “fund” (0.051) have high weights, strengthening fund supervision and market supervision and standardizing development and construction. In Topic 3, “leasing” (0.060) and “information” (0.045) are linked to build a leasing information and market system to adapt to the housing leasing needs of central cities in North China. To sum up, Shijiazhuang takes the balance between public rental housing and commercial housing as the starting point, relies on strong supervision and fund norms to ensure development, builds a leasing market system, and adapts to the diversified housing needs and market norms under the coordinated development of Beijing-Tianjin-Hebei.

The policy documents of Haikou can be categorized into five principal thematic areas. In Topic 1, “supervision” (0.064) and “fund” (0.057) work together to focus on the supervision of real estate funds and market supervision to ensure market stability. In Topic 2, “demolition” (0.028) and “construction” (0.024) cooperate to link urban renewal demolition and construction to adapt to the spatial expansion of free trade port cities. In Topic 3, “leasing” (0.023) and “housing security” (0.021) work together to strengthen the leasing and affordable housing system and guarantee the housing of people’s livelihood and talent. In Topic 4, “security” (0.036) and “leasing” (0.022) are linked to focus on the safety management of leased housing and protect the rights and interests of tenants. In Topic 5, “economic” (0.054) and “applicable” (0.054) work together to emphasize that real estate policies adapt to the economic development of free trade ports and flexibly adjust market rules. To sum up, Haikou takes fund supervision and market supervision as the shield, expands space through urban renewal, and relies on the leasing and security housing system to guarantee people’s livelihood, adapting to the flexibility and people’s livelihood security needs of real estate policies for the construction of Hainan Free Trade Port.

The policy documents of Wuxi can be summarized into five principal thematic domains. In Topic 1, keywords such as “subsidy” (0.019) and “commodity” (0.020) focus on the connection between policy subsidies and commercial housing, and activate the commercial housing market through policy tools. In Topic 2, “existing housing” (0.057) stands out in weight, and works together with “institution” (0.041) and “brokerage” (0.038) to focus on the transaction ecology of existing housing and revitalize the stock through institutional and brokerage services. In Topic 3, “green” (0.025) and “architecture” (0.019) work together to emphasize the integration of green buildings and real estate development to adapt to the construction of ecological cities. In Topic 4, “subcontract” (0.049) and “unit” (0.042) cooperate to focus on engineering subcontracting and construction unit management to standardize the development and construction process. In Topic 5, “housing security” (0.035) and “housing provident fund” (0.030) work together to strengthen housing security and provident fund support and guarantee people’s livelihood needs. In Topic 6, “credit” (0.041) and “enterprise” (0.034) are linked to build an enterprise credit system and standardize the behavior of real estate enterprises. To sum up, Wuxi coordinates to standardize the behavior of development construction and enterprises from multiple dimensions of policy, stock, and green, takes into account the activation of the commercial housing market and people’s livelihood security, and adapts to the positioning of the Yangtze River Delta advanced manufacturing and ecological livable city.

The policy documents of Tangshan reflect a comprehensive focus on five core thematic areas. In Topic 1, “leasing” (0.062) and “indemnificatory” (0.038) work together to focus on leasing and indemnificatory housing and guarantee people’s livelihood housing needs. In Topic 2, “fund” (0.076) and “transaction” (0.069) have high weights, strengthening fund supervision and transaction norms to ensure market stability. In Topic 3, “must” (0.010), “insure” (0.008), etc., work together to explore the real estate risk guarantee mechanism to adapt to the transformation needs of heavy industry cities. In Topic 4, “supervision” (0.087) and “presale” (0.045) are linked to focus on presale supervision and standardize the development and sales process. In Topic 5, “talent” (0.013) and “real estate” (0.012) work together to link talent with real estate adaptation and help urban transformation and talent introduction. To sum up, Tangshan takes leasing and security housing as the bottom line for people’s livelihood, relies on fund and transaction supervision to protect the market, explores risk mechanisms to adapt to the transformation of heavy industry, and relies on presale supervision and talent links to promote the real estate market to adapt to urban transformation and talent attraction needs.

The above six cities all emphasize the full-process coverage of the real estate market, including development, transaction, leasing, and security links, to ensure the full-cycle operation of the market. At the same time, supervision and risk prevention and control are also common focuses. Each city strengthens supervision and fund management to standardize enterprise behavior and control fund flow, so as to prevent market risks and meet the needs of urban economic development for the stability of the real estate market.

The differences are reflected in regional function adaptation and policy focus. Coastal cities such as Wuxi, Foshan, and Haikou rely on their respective economic circles and free trade port policies, and their policies focus more on market activation and industrial adaptation, such as promoting green buildings and leasing policies to meet the needs of industrial populations. Inland cities such as Nanchang, Shijiazhuang, and Tangshan pay more attention to people’s livelihood security and standardized development, such as paying attention to housing safety and public rental housing, as well as insurance mechanisms and talent policies related to industrial transformation, which are more in line with their regional economic foundations and development positioning. In terms of policy focus, manufacturing strong cities such as Foshan and Wuxi pay more attention to the housing adaptation of industrial populations and ensure the housing needs of industrial workers and talent through leasing and project development policies. Transformational cities such as Haikou and Tangshan focus on policy innovation and risk protection, and formulate policies closely around the urban development stage and transformation needs. Regional central cities such as Nanchang and Shijiazhuang take people’s livelihood security and market norms as the foundation, balance affordable housing and commercial housing, and strengthen supervision to adapt to regional radiation and people’s livelihood security functions.

In summary, the policy priorities of real estate governance vary across cities at different stages and types of economic development, with each city emphasizing issues aligned with its developmental needs. However, even within the same category of cities, differences in urban functions and strategic positioning lead to divergent developmental trajectories, which in turn shape the core themes of their real estate policies.

5. Conclusions

An analysis of policy documents issued by local governments across various Chinese cities, alongside a review of relevant academic literature, reveals that current real estate policies in China can be broadly categorized into four major domains: construction process, market regulation, housing affordability, and other policy areas.

The construction process dimension encompasses regulations and administrative measures related to the building and construction industry. This includes project evaluation, corporate credit assessment, information platform development, administrative oversight, engineering supervision, technical standards, and construction protocols. These policies reflect the government’s efforts to standardize management practices and provide technical support within the construction sector, thereby contributing to the orderly and sustainable development of the real estate market.

The category of market regulation aligns closely with classifications commonly discussed in the previous literature on real estate policy, particularly in relation to monetary policy, fiscal instruments, and macroprudential regulation. These policies are typically designed to guide market behavior, control financial risk, and ensure macroeconomic stability through targeted interventions in credit, taxation, and capital flows within the housing sector.

Housing affordability represents another critical policy area, aimed at maintaining housing market equilibrium and promoting inclusive urban development. On one hand, it involves rental market policies designed to expand access to housing and curb speculative demand. On the other hand, it includes policies related to the planning, construction, and distribution of affordable housing units. Collectively, these policies play an essential role in enhancing housing accessibility, supporting talent retention, and fostering long-term economic growth in urban areas.

Based on the results of keyword analysis and LDA topic modeling,

Table 3. Real estate policies of different city categories.

City Classification		Construction Process	Market Regulation	Housing Affordability	Other
First-tier cities	Beijing	Construction projects Engineering quality Technical standards	Taxation policies Social insurance Market transaction rules Financial supervision Purchase and loan restrictions	Rental subsidies Rental market management Real estate agencies Affordable housing
	Shanghai	Project evaluation Information platforms Construction supervision Technical standards Building regulations Construction credit management		Rental registration Affordable housing allocation
	Guangzhou	Construction and quality supervision		Rental applications Affordable housing Rent regulation	Housing expropriation and compensation
	Shenzhen		Housing provident fund Loan policies Real estate market control	Rental market supervision Rent regulation
New first-tier cities	Chengdu	Construction supervision	Provident fund loans Credit system development
	Hangzhou			Rental housing Financial oversight Housing security Rental market governance	Housing appraisal and quality management
	Nanjing			Rental market and funding management Talent housing and affordable housing	Property management and community services
	Wuhan		Pre-sale fund supervision	Rental market regulation Housing security	Housing information systems and platform governance
	Chongqing		Credit evaluation and market oversight	Housing rental and affordable housing	Urban–rural development
	Suzhou			Rental housing and housing security Income-based housing support policies
Cities with strong economy	Tianjin		Housing purchase restrictions and market control	Regulation of rental markets Affordable housing construction and allocation Housing renovation projects	Residential construction and facility management
	Qingdao			Talent housing Rental market oversight Housing funding regulation	Property rights management
	Zhengzhou	Construction and project supervision		Housing rental and transaction management
	Hefei	Construction and project supervision	Market order and funding supervision	Housing security and rental market
	Xiamen	Construction and project supervision	Housing management and financial oversight
Cities with economic development potential	Foshan	Construction and project supervision	Housing security and market regulation
	Wuxi	Construction and project supervision	Housing security and market regulation
	Nanchang			Affordable housing Rental market development Housing funding regulation	Property management
	Shijiazhuang		Pre-sale fund supervision Housing sales regulation		Registration and information systems
	Haikou		Housing transaction regulation Pre-sale fund supervision	Housing security and talent accommodation policies
	Tangshan	Construction project and engineering oversight	Pre-sale fund regulation, real estate market control	Housing security

provides a summary of real estate policy content across different categories of cities in China, illustrating the thematic focus and strategic priorities that characterize local policy responses.

It is evident that first-tier cities in China adopt relatively comprehensive real estate policy frameworks. These policies extend beyond mere market regulation to encompass the entire real estate value chain—including construction activities, market governance, affordable housing provision, and rental housing management. From a policy content perspective, first-tier cities not only articulate overarching governance strategies and policy directions for the real estate sector but also provide detailed operational guidelines to support implementation. This reflects their mature institutional capacity and the complex challenges they face in managing highly dynamic urban property markets. For first-tier cities, having already undergone a phase of rapid economic expansion, current real estate policies place greater emphasis on fostering high-quality development of the sector. This is achieved primarily through governance measures that stress regulatory oversight and the delineation of clear boundaries for market activity.

For new first-tier and economically strong cities, the pressing demand for sustained growth places particular emphasis on housing accessibility. Policies in these cities often integrate real estate measures with population and talent strategies. For example, home-purchase support programs targeting multi-child families not only stimulate reasonable housing demand but also encourage fertility and population growth. When combined with talent policies, such measures further enhance cities’ capacity to attract and retain skilled labor. For cities seeking rapid economic expansion, these policies thus serve multiple objectives simultaneously. In this sense, real estate policy functions as a mediating mechanism linking population policies with broader economic growth objectives.

In the case of cities with strong growth potential, real estate policies tend to be more context-sensitive and adapted to local conditions, reflecting both the specific stage of economic development and the unique socio-economic characteristics of each locality. In such cities, real estate policy is designed not only to address immediate housing needs but also to support long-term urbanization goals and inclusive economic transformation.

In sum, the heterogeneity of real estate policies reflects the interplay between cities’ developmental objectives, strategic orientations, and stages of economic growth. While cities at similar developmental stages tend to converge on comparable policy measures, their divergent growth trajectories and functional positioning generate more differentiated, city-specific approaches. This finding underscores the need to account for both structural commonalities and contextual particularities when analyzing urban real estate governance, offering insights for comparative urban studies as well as for the formulation of more adaptive and locally attuned policy frameworks.

6. Discussion and Future Research

6.1. Theoretical Contribution

This study conducts a comprehensive analysis of both academic literature and official policy documents related to real estate governance in China, with the aim of gaining a holistic understanding of the country’s real estate policy landscape.

Previous research has commonly categorized housing policies influencing the real estate market into three primary types: monetary policies, tax instruments, and macroprudential regulations. However, the findings of this study suggest that Chinese real estate policy extends beyond traditional market regulation. In addition to market-oriented tools, there is a strong emphasis on the entire real estate value chain, ranging from the construction process and associated sectoral oversight, to regulatory frameworks governing real estate transactions, as well as the protection of tenants’ and homebuyers’ rights and interests. These insights highlight the need for future research to broaden its analytical scope beyond market regulation alone, in order to better capture the multi-dimensional nature of real estate policymaking and its broader socioeconomic impacts.

Furthermore, this study conducts a comparative analysis of real estate policies across different categories of cities. The findings indicate that cities at different stages of economic development prioritize different aspects of real estate policy. First-tier cities, with their complex urban systems, tend to establish comprehensive, full-spectrum policies that span construction, market stability, and housing affordability—often accompanied by detailed operational guidance. In contrast, new first-tier and economically strong cities, which are experiencing rapid economic expansion, emphasize both market stability and affordable housing provision, using the latter as a strategic tool to attract and retain skilled talent. Their policies reflect the dual imperative of supporting urban growth while addressing social equity through improved housing accessibility.

6.2. Practical Contribution

The sustainable and healthy development of the real estate sector plays a vital role in promoting high-quality economic growth, safeguarding social stability, and ensuring public welfare. This study provides a comprehensive analysis of real estate policies in Chinese cities with strong economic potential, thereby offering valuable insights for policymakers to better understand current policy characteristics and to inform future policy design.

First, real estate policymaking must be city-specific. Local governments should adopt policies that are more flexible, diversified, and context-sensitive. Attention must be paid to the stage of economic development and the strategic objectives of each city, allowing for adaptive use of policy instruments—including regulatory measures, real estate development and planning, housing accessibility, and financial and fiscal tools.

Second, regulation should not only support growth but also prioritize high-quality development. For rapidly expanding and economically strong cities, as well as those with high development potential, it is insufficient to merely stimulate housing demand. Instead, proactive planning is essential. Drawing lessons from the policy evolution of first-tier cities can help avoid the pitfalls of “develop first, regulate later”.

Third, real estate policies should adopt diverse and coordinated forms. Policymakers should leverage the comparative advantages of different instruments while aligning them with local conditions and development needs. Cross-departmental collaboration—with agencies such as fiscal authorities, land and resources authorities and financial institutions—is necessary to form synergistic policy mixes. Such coordination can promote sustainable, stable, and healthy development of local real estate markets.

6.3. Limitations and Future Research

This study adopts a quantitative research approach to conduct an in-depth analysis of literature and policy documents related to real estate policy in China, with a particular focus on mapping the knowledge structure through scientific knowledge graph techniques. While the research provides a comprehensive overview of the thematic landscape and policy orientations within the Chinese real estate sector, it does not address the implementation outcomes or real-world impacts of these policies. Future research should therefore expand beyond textual and structural analysis to empirically evaluate the effectiveness and socio-economic consequences of real estate policy interventions.

The scope of this study is subject to certain limitations. On the one hand, the analysis primarily focuses on real estate policies in Chinese cities with strong economic potential, thereby reducing variation arising from large disparities in economic development levels. Future research should extend the scope to include a broader set of cities in order to capture a more comprehensive picture of urban real estate policymaking. On the other hand, to build a more holistic and globally representative understanding of real estate governance, future research should incorporate policy data from a wider range of regions and countries, with the aim of constructing a global knowledge graph of real estate policies.

Finally, through comparative analysis, this study reveals that the content and priorities of real estate policies vary significantly across different categories of cities, depending on their stage of economic development and urbanization. As such, future research should pay closer attention to regional heterogeneity, acknowledging the importance of localized policy design in shaping housing outcomes and market dynamics.