Real Estate

16 pages, 1566 KB

Open AccessArticle

Seasonality in the U.S. Housing Market: Post-Pandemic Shifts and Regional Dynamics

by Yihan Hu and Yifei Huang

Real Estate 2025, 2(4), 22; https://doi.org/10.3390/realestate2040022 - 15 Dec 2025

Viewed by 1275

Seasonality has traditionally shaped the U.S. housing market, with activity peaking in spring-summer and declining in autumn-winter. However, recent disruptions, particularly those following COVID-19, raise questions about shifts in these patterns. This study analyzes housing market data (1991–2024) to examine evolving seasonality and [...] Read more.

Seasonality has traditionally shaped the U.S. housing market, with activity peaking in spring-summer and declining in autumn-winter. However, recent disruptions, particularly those following COVID-19, raise questions about shifts in these patterns. This study analyzes housing market data (1991–2024) to examine evolving seasonality and regional heterogeneity. Using Housing Price Index (HPI) data, inventory, and sales data from the Federal Housing Finance Agency and U.S. Census Bureau, seasonal components are extracted via the X-13-ARIMA procedure, and statistical tests assess variations across regions. The results confirm seasonal fluctuations in prices and volumes, with recent shifts toward earlier annual peak (March–April) and amplified seasonal effects. Regional variations align with differences in climate and market structure, while prices and sales volumes exhibit in-phase movement, suggesting thick-market momentum behaviour. These findings highlight key implications for policymakers, realtors and investors navigating post-pandemic market dynamics, offering insights into the timing and interpretation of housing market activities. Full article

(This article belongs to the Special Issue Developments in Real Estate Economics)

► Show Figures

Figure 1

15 pages, 1201 KB

Open AccessFeature PaperReview

50 Years of Research in Real Estate Brokerage: A Semi-Systematic Literature Review

by Martin Ahlenius, Björn Berggren and Neville Hurst

Real Estate 2025, 2(4), 21; https://doi.org/10.3390/realestate2040021 - 4 Dec 2025

Viewed by 960

Abstract

Intermediaries are central to complex transactions. In housing markets, real estate brokers coordinate information flows, reduce search costs, and guide lay buyers and sellers through legal and financial steps. Despite this importance, scholarship on brokerage is dispersed across disciplines and methods. This paper [...] Read more.

Intermediaries are central to complex transactions. In housing markets, real estate brokers coordinate information flows, reduce search costs, and guide lay buyers and sellers through legal and financial steps. Despite this importance, scholarship on brokerage is dispersed across disciplines and methods. This paper presents a semi-systematic review of peer-reviewed articles published between 1970 and 2021. We map (i) study characteristics (country of origin and field), (ii) the distribution of units of analysis (individual, firm/organization, market), and (iii) the most frequently examined topics. Our synthesis indicates steadily rising academic interest but a fragmented knowledge base. We conclude by highlighting gaps—especially the scarcity of cross-country comparisons and the relative lack of qualitative and mixed-method studies on brokers’ practices and experiences. Full article

(This article belongs to the Topic Urban Science and Real Estate Dynamics: Insights into Housing, Finance, and Land Use)

► Show Figures

Figure 1

17 pages, 3622 KB

Open AccessFeature PaperArticle

BIM as a Social Technology to Enhance Governmental Decision-Making in Social Housing Programming

by Cristiano Saad Travassos do Carmo, Renata Gonçalves Faisca, Vitória Franco Benayon Menezes, Antonio Elias Amil Lisboa, Felipe Almeida de Sousa, Marcelo Jasmim Meirino and Patrícia Maria Quadros Barros

Real Estate 2025, 2(4), 20; https://doi.org/10.3390/realestate2040020 - 2 Dec 2025

Viewed by 724

Abstract

The housing deficit in developing countries is a common challenge, primarily impacting low-income populations. This paper investigated interinstitutional workflows using Building Information Modelling (BIM) as a social technology to improve the efficiency of design and construction stages in social housing projects. Following a [...] Read more.

The housing deficit in developing countries is a common challenge, primarily impacting low-income populations. This paper investigated interinstitutional workflows using Building Information Modelling (BIM) as a social technology to improve the efficiency of design and construction stages in social housing projects. Following a systematic literature review, process maps were developed and applied in a case study within a Brazilian urban community, located in a coastal city with a demographic density of 3602 inhabitants per square kilometre, involving a collaboration framework between a university and municipal authorities. Based on the party’s collaboration and precise cost estimation, the results indicate that this BIM-enabled collaboration supports the governmental decision-making process and leads to more effective resource management and optimised design costs, mainly during the design and construction phases. Therefore, this study concludes that digital modelling workflows are a powerful strategy for developing social housing projects because they facilitate the inclusion of families in the design and decision-making processes. Expanding this approach through integration with geospatial and public agency data is a promising area for future research, using such models in risk assessment policies and city urban planning. Full article

► Show Figures

Figure 1

19 pages, 1000 KB

Open AccessArticle

Transactional (Case–Shiller) vs. Hedonic (Zillow) Housing Price Indices (HPI): Different Construction, Same Conclusions?

by Mark Rzepczynski and Wei Feng

Real Estate 2025, 2(4), 19; https://doi.org/10.3390/realestate2040019 - 5 Nov 2025

Viewed by 2913

Abstract

Housing price indices (HPIs) are employed to assess the impact of the business cycle, monetary policy, housing policies, and local market dynamics. However, comparative empirical analysis of different HPI methodologies has not been conducted to measure why or when they may diverge and [...] Read more.

Housing price indices (HPIs) are employed to assess the impact of the business cycle, monetary policy, housing policies, and local market dynamics. However, comparative empirical analysis of different HPI methodologies has not been conducted to measure why or when they may diverge and whether these differences are meaningful. Two leading US HPI choices, the repeat-sale transactional (S&P Case–Shiller) and characteristic-based hedonic (Zillow) indices, although highly correlated, generate different distributions and time-series properties primarily at the city level. The spread between these two HPI choices measures the difference between housing market transaction intensity and a willingness-to-pay characteristic valuation. We find that transactional indices are more volatile, with HPI spreads associated with both macro and local drivers. The transactional index will rise more rapidly in a market with increased buying (positive macro and local market conditions) and fall further in a market with increased selling (negative macro and local market conditions) relative to a hedonic index. A buyer- or seller-biased spread between a transactional and hedonic housing price index (HPI) may impact policy judgments during housing market extremes. Full article

► Show Figures

Figure 1

26 pages, 5171 KB

Open AccessArticle

A Method to Measure Neighborhood Quality with Hedonic Price Models in Three Latin American Cities

by Marco Aurélio Stumpf González and Diego Alfonso Erba

Real Estate 2025, 2(4), 18; https://doi.org/10.3390/realestate2040018 - 3 Nov 2025

Viewed by 1602

Abstract

Location effects play a crucial role in the real estate market, encompassing aspects of accessibility and neighborhood quality. While traditional measures exist for accessibility, evaluating neighborhood quality can be a complex task. Understanding these elements is essential for accurately estimating property values, whether [...] Read more.

Location effects play a crucial role in the real estate market, encompassing aspects of accessibility and neighborhood quality. While traditional measures exist for accessibility, evaluating neighborhood quality can be a complex task. Understanding these elements is essential for accurately estimating property values, whether for commercial or tax purposes. Recently developed methods based on web scraping and automatic detection using artificial intelligence have proven effective but require substantial human and financial resources, often unavailable in small cities. As a solution, this study proposes and evaluates a simpler mechanism for assessing neighborhood quality using Google Street View images and a scoring system in a human-centered approach. Based on image interpretation, a set of weights is assigned to each point, resulting in a micro-neighborhood quality assessment. This study was conducted in three Latin American cities, and the resulting variable was integrated into hedonic price models. The findings demonstrate the feasibility and effectiveness of the proposed approach. The novelty of this study lies in applying a method based on quasi-objective criteria and adapted to cities with limited technological resources. Full article

► Show Figures

Figure 1

22 pages, 1356 KB

Open AccessArticle

A Holistic Sustainability Evaluation for Heritage Upcycling vs. Building Construction Projects

by Elena Fregonara, Chiara Senatore, Cristina Coscia and Francesca Pasquino

Real Estate 2025, 2(4), 17; https://doi.org/10.3390/realestate2040017 - 8 Oct 2025

Viewed by 962

Abstract

The paper contributes to the debate on the holistic sustainability assessment of real estate projects, integrating economic, financial, environmental, and social aspects. A methodological study is presented to support decision-making processes involving the preferability ranking of alternative investment scenarios: new building production vs. [...] Read more.

The paper contributes to the debate on the holistic sustainability assessment of real estate projects, integrating economic, financial, environmental, and social aspects. A methodological study is presented to support decision-making processes involving the preferability ranking of alternative investment scenarios: new building production vs. retrofitting the existing stock, in the context of urban transformation interventions. The study integrates life cycle approaches by introducing the social components besides the economic and environmental ones. Firstly, a composite unidimensional (monetary) indicator calculation is illustrated. The sustainability components are internalized in the NPV calculation through a Discounted Cash-Flow Analysis (DCFA). Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) are suggested to assess the economic and environmental impacts, and the Social Return on Investment (SROI) to assess the intervention’s extra-financial value. Secondly, a methodology based on multicriteria techniques is proposed. The Hierarchical Analytical Process (AHP) model is suggested to harmonize various performance indicators. Focus is placed on the criticalities emerging in both the methodological approaches, while highlighting the relevance of multidimensional approaches in decision-making processes and for supporting urban policies and urban resilience. Full article

(This article belongs to the Topic Improving Nature-Smart Policies through Innovative Resilient Evaluations)

► Show Figures

Figure 1

18 pages, 1257 KB

Open AccessArticle

Forecasting the Housing Market Sales in Italy: An MLP Neural Network Model

by Paolo Rosato and Matteo Galante

Real Estate 2025, 2(4), 16; https://doi.org/10.3390/realestate2040016 - 2 Oct 2025

Viewed by 2470

Abstract

Using panel data on 99 Italian provinces in the period between 2005 and 2020, the research investigates the effects of fundamental economic factors on the home sales at the provincial level, in order to build a forecasting model using a non-linear artificial intelligence [...] Read more.

Using panel data on 99 Italian provinces in the period between 2005 and 2020, the research investigates the effects of fundamental economic factors on the home sales at the provincial level, in order to build a forecasting model using a non-linear artificial intelligence approach (MLP-Multiple Linear Perceptron neural network). There are multiple objectives to this: (a) to test the hypothesis that national, regional and local fundamentals such as interest rates, income, inflation rate, unemployment and demography affect the activity’s degree of the housing market; (b) to verify the effectiveness of a neural network in describing the dynamics of the real estate market; (c) to build a simulation model capable of predicting the effect of changes in fundamentals, also due to economic policy measures, on the market. Empirical results show that neural networks offer better capabilities than linear models in representing the complex relationships between the economic situation and the real estate market. The study provides useful information for regulators to improve the effectiveness of monetary policy to stabilize real estate markets as well as for stakeholders to draw up scenarios of market development. Full article

(This article belongs to the Topic Improving Nature-Smart Policies through Innovative Resilient Evaluations)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Real Estate, Volume 2, Issue 4 (December 2025) – 7 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI