A Study on the Price Spatial Differentiation and Influencing Factors of Rural Homestay in Suzhou Based on the Hedonic Price Model

Abstract

Using ArcGIS software to conduct spatial autocorrelation analysis, it was found that the price distribution of rural homestays in Suzhou is not random but shows apparent spatial clustering. Among the 81 rural homestays were 9 high-high clusters, 25 low-low clusters, 1 high-low outlier, and 5 low-high outliers. The hedonic price model is an economic model that breaks down goods or services into multiple attributes to estimate their prices. Based on this model, this paper further investigates the differentiated impacts and interactions between internal accommodation characteristics and external environmental attributes in the pricing mechanism of rural homestays in Suzhou. The results show that (1) the high-priced rural homestays in Suzhou are concentrated in the lake island resort with good natural environment and perfect infrastructure; (2) the location, facility quality, and network score of rural homestays have a positive impact on housing prices; (3) the parking lot of rural homestays hurts housing prices; (4) the quality of rural residential buildings and facilities has the most significant positive impact on housing prices. By comparing the hedonic price model of rural homestay and the heterogeneous preferences of consumers, this study provides rural homestay operators with effective room pricing strategies and marketing implications, provides policymakers with reference factors to promote the development of rural homestay industry, and has specific guiding significance for the sustainable development of the homestay industry.

1. Introduction

Rural homestays refer to small-scale accommodation facilities that utilize rural residences and related resources, with the hosts participating in operational services to provide tourists with experiences of local nature, culture, ways of life, and production [1]. Tourism is an effective development tool [2] (pp. 878–889). The development of rural tourism has altered the lifestyles of rural residents [3] (pp. 138–151), simultaneously enhancing their living standards and promoting economic growth in rural areas [4] (pp. 1438–1441) [5] (pp. 239–248). As a primary component of rural tourism, rural homestays represent a significant means of driving rural economic growth and sustainable development [6] (pp. 223–233) [7] (p. 2964). Since the rapid expansion of China’s domestic tourism market in 2010, homestays have emerged sporadically. Between 2014 and 2019, the homestay industry witnessed rapid development, with the market size continuously expanding at an average annual double-digit growth rate. Current metrics indicate a 2023 market valuation of CNY 3.313 billion (≈USD 456 million), supporting 12,338 employees in homestay services nationwide [8].

The Suzhou Municipal Government places significant emphasis on the high-quality development of the rural homestay industry. Since 2019, a specialized institution has been established annually to select outstanding rural tourism homestays in Suzhou. By establishing exemplary cases, this initiative promotes healthy competition among rural homestays. Moreover, Suzhou has abundant rural tourism resources and is a representative region of Chinese gardens and the Jiangnan water town culture. The design of Suzhou’s homestays reflects distinct local characteristics, with rural areas featuring numerous ancient villages, towns, and gardens. The diversity among different homestays facilitates hedonic price model analysis. The rural homestay business exhibits rich diversity, with accommodations distributed around lakes, islands, and mountains. Both experienced and inexperienced operators coexist, as do high-end boutique and economy homestays. Consequently, this study selects rural homestays in Suzhou as the research object, analyzing the distribution characteristics and influencing factors of rural homestays in typical traditional Chinese village and town tourism cities while exploring the uniqueness of Suzhou.

The hedonic price model constitutes the theoretical foundation of this study. This research expands upon the theory by incorporating the influence of parking facilities and online ratings of rural homestays into the hedonic price model, enhancing related theories regarding rural homestay pricing. The study employs questionnaire surveys, GIS cluster analysis, and regression equations to investigate market demand, spatial differentiation, and pricing factors of rural homestays, further refining research on rural homestay prices.

This research makes the following contributions. First, it proposes a pricing model for rural homestays using the hedonic price model to analyze factors influencing room prices. Second, although the GWR model did not demonstrate significant advantages over the traditional OLS model in this study, the research acknowledges the promoting effect of GWR in the hedonic price model. Third, the study examines and discusses factors affecting housing prices in rural homestays, concluding that operators can enhance market competitiveness by improving the quality of facilities in rooms and public areas. Online ratings also positively impact rural homestay prices, as higher service quality encourages favorable consumer reviews at a lower cost. It is advisable to avoid constructing private parking lots within rural homestays to preserve limited courtyard activity space. Finally, the research findings suggest that relevant government departments should prioritize environmental protection and infrastructure upgrades in rural areas to enhance the profitability of rural homestays.

2. Literature Review

2.1. The Internal Influencing Factors of Rural Homestay Pricing

Existing research demonstrates that multiple internal factors influence the pricing mechanism of rural homestays. For instance, property ratings, analogous to those in the traditional real estate sector [9] (pp. 405–424) [10] (pp. 1036–1043), and the number of rooms [11] (pp. 184–196) [12] (pp. 126–137) remain core indicators. Consequently, higher property ratings correlate with more expensive homestays. Similarly, the number of rooms significantly influences pricing [13] (pp. 2240–2260) [14] (pp. 46–56) [15] (pp. 302–308) [16] (pp. 533–543) [17] (pp. 2405–2424) [18] (pp. 120–131). Quantitative studies further indicate that a 1% increase in rooms corresponds to a 10.52% increase in house prices. In contrast, for rural homestays, a 1% decrease in beds correlates with a 12.32% increase in house prices [19] (pp. 1–11).

Qualitative research by Zulkefli, Aziz, and Radzol highlights the importance of unique product design as a critical factor influencing homestay development. Integrating distinctive homestay designs with local culture enhances tourist appeal [20] (pp. 256–270). Homestays targeting a larger pool of potential tourists may command higher prices. Additionally, the types of activities offered by rural homestays influence pricing, as some consumers seek opportunities to learn about local culture and lifestyles [21] (pp. 1059–1072).

Safety represents a key criterion for tourists when selecting accommodations [22] (p. 21). Consumers prioritize safety and risk management when experiencing homestays [23] (p. 102108). Enhanced safety measures build consumer trust and willingness to pay premium prices for secure homestay experiences [24] (pp. 53–72). However, security guarantee, a factor that has been confirmed by numerous studies to directly influence tourists’ satisfaction and their willingness to repurchase [25] (pp. 162–179) [26] (p. 936), has been overlooked in all research on rural homestay pricing due to the challenge of quantifying safety indicators.

Brand reputation positively impacts homestay pricing. Consumers are more likely to accept homestays managed by reputable brands or enterprises, paying higher prices due to perceived reliability and reduced information search effort [27] (pp. 83–103). Furthermore, consumers often align their preferences with homestay brands reflecting their characteristics, potentially leading to a higher willingness to pay [21] (pp. 1059–1072).

Qiu et al. emphasize that both domestic and international evaluation standards highlight the correlation between the quantity and quality of service staff and rural homestay prices [28] (p. 103792). The price of a rural homestay tends to increase with a larger number of service staff and higher service quality [19] (pp. 1–11).

Notably, while current evaluation systems address general indicators such as cleanliness and communication efficiency [28] (p. 103792), the dimensions of sustainable operation, including resource protection and marketing promotion, require further exploration [26] (p. 936). Existing research inadequately reflects the unique characteristics of rural homestays, such as consistency between online descriptions and actual conditions [28] (p. 103792). Bhalla and Bhattacharya underscore tourists’ satisfaction and experience as pivotal drivers of destination selection, local product consumption, and repeat visits [25] (pp. 162–179). However, the relationship between online ratings reflecting tourist satisfaction and rural homestay prices remains under-researched. Supporting facilities [26] (p. 936) and spatial layout/atmosphere creation [29] (pp. 43–53) constitute key differentiators in competition, yet their impact on rural homestay prices requires further investigation. Therefore, this study focuses on two internal factors, online ratings, and supporting facilities to explore rural homestay pricing mechanisms.

2.2. The External Influencing Factors of Rural Homestay Pricing

External factors primarily emphasize location attributes. The proximity to transportation hubs, such as airports [30] (pp. 186–197) and highways [31] (pp. 10–15), enhances the convenience and subsequently increases the prices of homestays. Rural homestays with convenient transportation tend to have higher prices [28] (p. 103792) [32] (p. 102618) [33] (p. 102861). Additionally, the closer a homestay is to a high-speed railway station, the higher its room rate tends to be. However, for high-quality rural homestays, the distance from an expressway toll station positively correlates with the price [19] (pp. 1–11).

Geographical factors, including natural landscapes such as sea view resources [34] (pp. 87–99) or the uniqueness of surrounding scenic spots [33] (p. 102861) [35] (p. 09007), significantly contribute to increased homestay prices. Some studies indicate that rooms near scenic spots are more expensive [10] (pp. 1036–1043) [34] (pp. 87–99). Conversely, other research on Chinese rural homestays suggests that the farther high-quality homestays are from scenic spots, the more expensive they become [19] (pp. 1–11).

Furthermore, the community in which a homestay is located impacts the pricing mechanism of rural homestays. The lease prices of real estate and land in the community where the homestay is located positively influence rural homestay prices [33] (p. 102861). Factors such as the local community’s crime rate and population density play a role. A study found that the number of community cases had no significant effect on homestay prices [29] (pp. 43–53). Unlike urban homestay studies [36] (pp. 114–124) [37] (pp. 773–789) [38] (pp. 89–101), excessive population density negatively affects rural house prices due to tourists seeking less congested environments. Urban tourists prefer staying in densely populated city centers for easy access to sightseeing, shopping, commercial areas, and entertainment venues. Consequently, the closer to the city center, the higher the homestay price [34] (pp. 87–99) [39] (pp. 127–131) [40] (pp. 278–291) [41] (pp. 2695–2715). In contrast, rural tourists favor communities with lower population densities, leading to higher occupancy rates and prices for rural homestays located in such areas [29] (pp. 43–53).

The ecological environment and natural experiences enhance attractiveness, thereby increasing homestay prices—better environmental quality results in a more pronounced premium effect and higher room rates. For mountainous rural homestays, the slope positively influences prices due to higher altitudes, increased construction costs, and sufficient sunlight [19] (pp. 1–11).

Additionally, negative stereotypes about homestays, particularly regarding poor environments, hygiene conditions, and remote locations, affect pricing [21] (pp. 1059–1072). However, insufficient attention has been given to the ecological resource integration capacity of rural homestays [42] (pp. 242–249), community participation, and influence [43] (p. 050006). Some studies argue that current research may not fully capture the characteristics of rural homestays [28] (p. 103792).

2.3. Hedonic Price Model of Rural Homestay

Rosen proposed the hedonic price model, which uses objective values and characteristics to describe the differentiation of products [44] (pp. 34–55). The hedonic price model is based on a linear regression equation, which is used to model the relationship between the dependent variable (the price attribute of the product) and a set of independent variables (the objective attribute of the product) [10] (pp. 1036–1043) [45] (pp. 105–133). Thus, the purpose of the hedonic price model is to assess the relationship between the market value of a composite product and each attribute [11] (pp. 184–196) [34] (pp. 87–99).

The hedonic price model has been extensively applied in property valuation and hospitality pricing research [34] (pp. 87–99) [46] (pp. 7–27) [47] (pp. 11–26) [48] (pp. 102–118). Its implicit price estimates effectively capture consumers’ authentic willingness to pay, establishing an objective methodological framework for analyzing homestay pricing determinants through revealed preference mechanisms [49] (pp. 140–147).

3. Current Demand for Rural Homestay in China

This study collected statistics on the demand for rural homestays through a questionnaire survey. The questionnaires were distributed through the Internet by random sampling. A total of 1005 valid questionnaires were collected.

Reliability analysis was performed using Cronbach’s alpha for all the questionnaires recovered. Cronbach’s alpha was developed by Lee Cronbach in 1951 to measure the internal consistency of a test or scale to ensure validity [50] (pp. 297–334). The value of alpha is expressed as a number between 0 and 1. In general, the higher the coefficient, the higher the method’s reliability. In basic research, the reliability should be at least 0.80 to be acceptable. K is the number of questions in the questionnaire; S_i represents the difference between the score on each item and the score on question i, and S_x is the variance of the total score for all questions; the above values are input into Cronbach’s alpha formula:

$$\alpha ={\displaystyle \frac{K}{K-1}}\left(1-{\displaystyle \frac{\sum S_i^2}{S_x^2}}\right)$$

(1)

The resulting alpha value is 0.850, which shows that the questionnaire survey scale is reliable.

The questionnaire results show that 34.33% of the respondents’ income is CNY 4001–8000, accounting for the highest proportion. It was followed by CNY 2001–4000, with a proportion of 27.46% (Figure 1). According to the report “2024 Resident Income and Consumption Expenditure in China” released by the National Bureau of Statistics of China in 2025, the per capita monthly disposable income of the low-income group is CNY 759.17, lower middle-income group CNY 1800.67, middle-income group CNY 2827.05, upper middle-income group CNY 4446.58, and high-income group CNY 8234.08 [51]. This report suggests that most respondents’ incomes are concentrated in the middle. Therefore, the questionnaire survey results are consistent with the consumption level of the vast majority of Chinese consumers, and the conclusions related to the pricing of rural homestays are relatively objective.

Through the analysis of questionnaire data, it is evident that when respondents select rural homestay, the primary factor they consider is housing price, which accounts for 51.95% (Figure 2). The calculation selected the weighted average method to obtain the score of each influence factor and then drew the radar chart according to the score. Consequently, in descending order of importance, the factors influencing respondents’ choice of rural homestay are location, transportation, room rate, safety, service quality, distinctive design, sanitary condition, rating, and others (Figure 3).

After the data analysis of 780 respondents who had chosen to stay in a rural homestay, it was found that with the increase in homestay prices, the top three factors of concern to respondents changed from health, price, and safety to service, design, and location (Figure 4). In addition, the questionnaire results showed that 61.03% of the respondents were more inclined to stay in rural homestays below CNY 400. In total, 27.44% of the respondents felt that the actual price was higher than expected after staying, while 30.51% believed that the price and content of rural homestays did not match.

Through cross-analysis of each option of the questionnaire, it can be found that there is significant heterogeneity in consumers’ preferences for high-priced rural homestays, and the influencing factors present the following logical correlations. First of all, the objective conditions of consumers directly affect their willingness to pay. Age and income level are positively correlated with acceptance of high-priced homestays; older and higher-income groups have a higher acceptance of high-priced homestays. This phenomenon may be related to the stronger payment capacity of high-income groups. They are more inclined to choose resort-type rural homestays to meet the pursuit of unique experiences and are willing to pay a premium for it. Secondly, the characteristics of consumption behavior further reinforce the differences in preferences. Consumers with extended vacation periods are more likely to accept high-priced homestays because their vacation needs are more inclined toward profound experiences rather than short stays. It is worth noting that those who have never participated in rural tourism and those who have explicitly stated that they “do not want to stay in rural homestays” show higher acceptance of high-priced homestays. This contradiction may stem from the former holding idealized expectations due to a lack of practical experience in rural homestays. At the same time, the latter may equate high prices with quality guarantees, thereby reducing psychological resistance. Finally, consumption experience influences decisions through the trust mechanism. Consumers who stay frequently or have a purchase experience are significantly more receptive to rural homestays. Such groups have formed a sense of trust due to their past positive experiences. They are more likely to make repeat consumption decisions and may be more willing to try high-priced products because of their familiarity.

In conclusion, the heterogeneity of consumers’ preferences for high-priced rural homestays results from the combined effect of objective conditions, behavioral characteristics, and accumulated experience, which provides an important basis for homestay operators to segment the market and set differentiated prices.

4. Analysis of Spatial Pattern of Rural Homestay Prices in Suzhou

This study selects rural homestays in Suzhou as the research object, which is unique from multiple perspectives. First of all is the innovation and representativeness of the management model. Suzhou Rural Homestay breaks away from the traditional interactive operation of homestays [52] (pp. 49–58) and adopts a hotel-like management model to form a large-scale operation system. The composition of its operators is both local and open: some indigenous people follow the traditional homestay model [53,54]. There are also many commercial entities where foreign capital enters the market by leasing houses and land. This dual structure provides a unique sample for studying the homestay business format.

Secondly, on the demand side, the core motivation for tourists to choose Suzhou rural homestays is to deeply experience the architectural culture of the Jiangnan water town and to vacation in the traditional cultural way of local rural community life [55] (pp. 407–421). This phenomenon is because rural lifestyle and cultural activities attract modern tourists [53]. This cultural consumption demand is highly consistent with the supply advantages of Suzhou as the core area of Chinese garden culture, an outstanding representative of Chinese water town culture, and a place rich in ancient village and town resources. Meanwhile, rural homestays in Suzhou feature both a wide variety of business types and differentiated prices; geographical distribution differences such as being surrounded by lakes, islands, and mountains, product stratification between high-end boutique and economy homestays, and the uneven distribution of operators’ experience levels provide an ideal data basis for constructing a hedonic price model.

From a global perspective, at present, South American and African countries are still affected by the disadvantaged status of rural communities, resulting in the lagging development of rural tourism [56] (pp. 562–567). Rural tourism in some developing countries still often faces the predicament of community empowerment. For example, Ecuador needs to rely on external knowledge input to enhance participation [57] (pp. 201–229). Although rural tourism policies in the Asia–Pacific region are generally efficient (such as countries like Malaysia and Thailand enhancing their competitiveness through government leadership) [58] (p. 21582440211007117), Suzhou still effectively avoids the interference of weak infrastructure on the pricing mechanism by relying on its complete rural infrastructure and mature management system. Hence, the research focus is more on the interaction between rural homestays’ internal and external influencing factors and the market rules.

4.1. Data Sources of Rural Homestay in Suzhou

The hedonic price model is variable over time [59] (pp. 763–819). Corresponding to this, the price of a homestay facility and availability on working days, holidays, and even between different seasons change [60] (pp. 1113–1118) [61] (pp. 195–199). In order to obtain a more realistic hedonic price model, the price of a rural homestay must be obtained under a unified sampling standard [59] (pp. 763–819). Therefore, to meet the above requirements, the prices of rural homestays in Suzhou, as determined by the research, are all standard room prices for non-weekends and non-holidays. This study selects data on rural homestay from Ctrip (www.ctrip.com (accessed on 11 September 2024)), a mainstream Online Travel Agency platform in China. Data crawling through Python (3.9.0) was used to obtain the characteristic information data of rural homestays in popular areas of Suzhou City. The following is the data acquisition of rural homestays in popular areas of Suzhou City (

Table 1. Crawling results of rural homestay data in Suzhou.

Crawl Area	Crawl Pages	Number Each Page Shows
Taihu Lake Scenic Area	82	A single page shows 15 homestays
Wujiang District	59	A single page shows 15 homestays
Xishan Island	59	A single page shows 15 homestays
Manshan Island	1	A single page shows 4 homestays
East Taihu Lake Scenic Area	26	A single page shows 15 homestays
Jinxi Ancient Town	66	A single page shows 15 homestays

).

Ctrip repeated booking information for the same homestay on different pages, and some of the homestays on the page were not located in rural areas. Therefore, manual screening removed some duplicate and irrelevant data. Finally, the collated database contains the price and characteristic information of 81 rural homestays in Suzhou. There is a large gap between rural homestay prices’ lowest and highest value, with the lowest price of CNY 56 and the highest price of CNY 3486. The comprehensive analysis shows that the rural homestays with higher prices (priced above CNY 1000) are mostly high-end hotel-type rural homestays. The building consists of a single-family house with a courtyard. The main business is natural person landlords, and a few companies operate. The low-cost homestays are mainly rural homestays designed by traditional fast hotels, and the main business is the natural person landlord. At the same time, the functions provided by low-cost homestays are only simple overnight accommodation, lacking other essential functions of homestays.

Many rural homestays in Suzhou cost more than CNY 1000 per night. These high-end homestays usually have a variety of high-end activity facilities, such as swimming pools, which are dispersed and lack major competitors. Comparative analysis shows that the pricing of rural homestays without major competitors will be higher than that of homestays with major competitors.

4.2. Results of Global Spatial Autocorrelation Analysis

The analysis of the power proves that the calculation results are compelling under extreme conditions when the sample size is greater than 48 (

Table 2. Power analysis results.

Alpha Value (Type I Error)	Beta Value (Type II Error)	Power Value (1—Beta Value)	Sample Capacity
0.05	0.000	1.000	48.000

). The sample size of this study is 81, so the results of the following calculation are significant.

The spatial autocorrelation (Moran’s I) of ArcGIS was used to analyze the price distribution of 81 rural homestays in Suzhou. The results showed that Moran’s Index was 0.127920, and the z-score was 2.267363. The p-value is 0.023368. The z-score value is between 1.96 and 2.58, and the possibility of randomly generating this clustering pattern is less than 5%. The p-value is 0.023368, indicating that the spatial pattern of the price distribution of rural homestays in Suzhou cannot possibly appear randomly, and the probability of randomly generating this clustering pattern is 2.3368%.

In conclusion, the prices of country houses in Suzhou are not randomly distributed in space, and there is a significant spatial relationship. The value of Moran’s Index ranges from −1.0 to 1.0, and the Moran’s Index of 81 rural homestays in Suzhou is 0.127920. When Moran’s Index is greater than 0, it indicates a positive spatial correlation. The larger the value, the more pronounced the spatial correlation, which indicates that the price of rural homestays in Suzhou is positively correlated with the spatial distribution of homestays, showing an apparent clustering trend, indicating that rural homestays with similar pricing in Suzhou show a specific spatial clustering:

• The price of rural homestays in Xishan Island, Suzhou City, showed the result of a High Cluster; that is, the price of rural homestays in this area was generally high (Figure 5).
• The analysis results show that rural homestays form 9 clusters with high prices—that is, their prices and the prices of neighboring homestays are high; 25 low-price cluster points—that is, their prices and the prices of neighboring homestays are low; 1 high outlier points—that is, their prices are high while the prices of neighboring homestays are low; and 5 low outlier points—that is, their prices are low, and the prices of neighboring homestays are high (Figure 5).

There are high-price agglomeration points and low-price agglomeration points in Suzhou, indicating that the local agglomeration effect of the price distribution of rural homestays in Suzhou is apparent. The appearance of high price dispersion points indicates that although there is a spatial agglomeration of all kinds of homestay prices, the agglomeration situation significantly differs, and there is an apparent spatial heterogeneity. The above conclusions prove that rural homestays in Suzhou are affected not only by external factors such as location and environment but also by internal factors such as their supporting facilities. Therefore, the spatial model of price distribution of rural homestays in Suzhou needs further discussion.

Through exploratory regression, adjusted R-squared was found to be 0.60, the maximum VIF was 4.83, and there was no obvious collinearity in the data. The adjusted R-squared of the Ordinary Least Square (OLS) was 0.6019. The adjusted R-squared of Geographically Weighted Regression (GWR) was 0.6021. The AICc value was used to evaluate the optimization effect of the model. Compared with OLS, the AICc value of GWR was only reduced by more than 0.0007. Adjusted R-squared and AICc of GWR were better than the OLS, but the difference was too small and not statistically significant. The above research results indicate local heterogeneity in the price of rural homestays in Suzhou, but the local heterogeneity is not significant (Figure 6).

5. Analysis of Influencing Factors of Spatial Differentiation of Rural Homestays in Suzhou

5.1. Construction of Hedonic Price Model of Rural Homestays in Suzhou

The study uses the hedonic price method for analysis. The hedonic price method applies the hedonic price theory and the model to find out the price implied by the quality factors that affect the price of rural homestays, and can determine the degree of influence of the change in each quality factor on the price of rural homestays [49] (pp. 140–147). Consumer demand for rural homestay products is based on the nature of rural homestay itself and includes the internal and external characteristics of rural homestay products [19] (pp. 1–11). Consumers’ purchase of a homestay product is influenced by external and internal characteristics. The hedonic price model expresses the price of rural homestays as a function representation of various attribute levels that the product needs to contain. Therefore, we can study the specific factors and influence weights that influence the premium of rural homestays in Suzhou.

5.2. Selection and Index Quantification of Influencing Factors of Rural Homestay Price in Suzhou

The study based on the above literature research on the factors affecting the price of homestays, combined with the different characteristics of the spatial distribution of rural homestays, and combined with the Ctrip platform crawled data, finally identified 11 key indicators for the evaluation of rural homestays: (1) location of homestays, (2) number of scenic spots, (3) rating of scenic spots, (4) public transportation, (5) parking lot, (6) courtyard space, (7) landscape platform, (8) outdoor activities, (9) room facilities, (10) public facilities, (8) Internet ratings. The above indicators are quantified below (

Table 3. Classification and scores of evaluation indexes of rural homestays in Suzhou.

Evaluation Index	Metric Standard
Location	Within the scenic area (1) Not in the scenic area (0)
Number of attractions	Number of attractions within 1 km (per)
Level of tourist attractions	National 5A level tourist attraction (3) National 4A level tourist attraction (2) National 3A level tourist attraction (1) National 2A level tourist attraction and below (0)
Public transport	Public transport stations within 500 m (1) No public transport stations within 500 m (0)
Parking lot	Private parking lot (2) Public parking lot (1) No parking lot (0)
Courtyard space	Private courtyard space (2) Public courtyard space (1) No courtyard space (0)
Viewing platform/balcony	With viewing platform/balcony (1) No viewing platform/balcony (0)
Outdoor activity	With outdoor activity area (1) No outdoor activity area (0)
Room facilities	Feature facilities (2) Only conventional facilities (1) Inadequate facilities (0)
Public area facilities	Free value-added services (2) Paid value-added services (1) No value-added services (0)
Online rating	4.8–5.0 points (3) 4.0–4.7 points (2) 3.0–3.9 points (1) Below 3.0 points (0)

).

An exploratory factor analysis was conducted on the 11 quantified evaluation indicators to extract the main factors. After varimax rotation, the factor loading coefficients were obtained (

Table 4. Distribution of factor loading coefficients after varimax rotation.

Evaluation Index	Factor Loading Coefficients					Communality
	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5
Location	0.069	0.564	−0.140	−0.473	−0.527	0.844
Number of attractions	0.113	0.897	0.072	−0.036	−0.034	0.826
Level of tourist attractions	−0.018	0.928	−0.120	−0.165	0.048	0.905
Public transport	−0.061	−0.180	0.884	0.101	−0.046	0.830
Parking lot	−0.004	−0.184	0.133	0.930	0.012	0.917
Courtyard space	0.804	0.307	0.009	0.133	0.118	0.773
Viewing platform/balcony	0.439	0.446	0.058	0.419	0.317	0.671
Outdoor activity	0.862	−0.133	0.153	−0.027	0.113	0.797
Room facilities	0.883	0.042	0.028	−0.059	0.113	0.798
Public area facilities	0.304	0.039	−0.030	0.011	0.917	0.935
Online rating	0.240	0.139	0.840	0.074	0.059	0.792

). Main factor 1 correlates relatively highly with courtyard space, landscape platforms, outdoor activities, and room facilities and can represent architectural features. Main factor 2 has a relatively high correlation with the location of the homestay, the number of scenic spots, and the grade of the scenic area, representing location features. Main factor 3 has a relatively high correlation with online ratings and can represent operational features. Main factor 4 has a relatively high correlation with parking lots and can represent transportation features. Main factor 5 has a relatively high correlation with public area facilities and can represent facility features. The correspondence between the public transportation research item and factor 3 contradicts the expectation. A possible reason is that the rural bus routes are limited, and the bus stops are relatively concentrated, resulting in insufficient discrimination of the evaluation indicators and interference with the evaluation results. Therefore, the public transportation evaluation indicator was deleted. Finally, the five main factors affecting the rural homestay pricing in Suzhou are summarized (

Table 5. The five factors affecting the rural homestay pricing in Suzhou.

Influencing Factor	Evaluation Index
Architectural features (X1)	Courtyard space
	Viewing platform/balcony
	Outdoor activity
	Room facilities
Location features (X2)	Location
	Number of attractions
	Level of tourist attractions
Business features (X3)	Online rating
Traffic features (X4)	Parking lot
Facility features (X5)	Public area facilities

).

5.3. Establishing the Hedonic Price Model of Rural Homestays in Suzhou

Take X₁, X₂, X₃, X₄, and X₅ as the independent variables (Table 4) and the price as the dependent variable, and bring them into the exponential linear model of the hedonic price:

$$P_{it}={\alpha }_0+{\displaystyle \sum }_{j=1}^N{\beta }_j\cdot X_{ijt}+{\displaystyle \sum }_{t=1}^T{\lambda }_t\cdot C_t+{\epsilon }_{it}$$

(2)

Because the value of price as a dependent variable is too large compared with the value of the evaluation index of the independent variable, the accuracy of the hedonic price model is affected. Therefore, the semi-logarithmic model of the hedonic price index is used to analyze the logarithm of the price of the dependent variable:

$$\ln P_{it}={\alpha }_0+{\displaystyle \sum }_{j=1}^N{\beta }_j\cdot X_{ijt}+{\displaystyle \sum }_{t=1}^T{\lambda }_t\cdot C_t+{\epsilon }_{it}$$

(3)

Linear multiple regression analysis using SPSS (R26.0.0.0) yielded the following results (

Table 6. Results of linear regression analysis of rural homestay prices in Suzhou (n = 81).

	Unstandardized Coefficient		Standardized Coefficient	t	p	Collinearity Diagnostics
	B	Standard Error	Beta			VIF	Tolerance
Constant	4.813	0.291	−	16.554	0.000 **	−	−
X1	0.457	0.060	0.674	7.651	0.000 **	1.518	0.659
X2	0.027	0.038	0.058	0.730	0.468	1.242	0.805
X3	0.009	0.090	0.008	0.104	0.918	1.140	0.877
X4	−0.253	0.149	−0.133	−1.700	0.093	1.197	0.835
X5	0.314	0.151	0.170	2.080	0.041 *	1.307	0.765
R-squared	0.617
Adjusted R-squared	0.591
F	F (5,75) = 24.146, p = 0.000
D-W value	1.877

* p < 0.05, ** p < 0.01.

).

The R-squared of the model is 0.617, which means that X₁, X₂, X₃, X₄, and X₅ can explain 61.7% of the change in rural homestay prices in Suzhou. The result of the F test indicates that at least one of the five independent variables X₁, X₂, X₃, X₄, and X₅ will impact on the rural homestay prices. In addition, according to the multicollinearity test of the model, it is found that all the VIF values in the model are less than 5, which means that there is no collinearity problem. Moreover, the D-W value is near the number 2, indicating no autocorrelation in the model and no correlation between the sample data. The above calculation results all prove that the model is accurate. Therefore, X₁, X₂, X₃, X₄, and X₅ can be taken as independent variables, and the price of rural homestays in Suzhou can be taken as dependent variables to form the following equation:

$$\ln P=4.813+0.457X_1+0.027X_2+0.009X_3-0.253X_4+0.314X_5$$

(4)

The p-value of X₁ is less than 0.01, and the p-value of X₅ is less than 0.05, indicating that these two independent variables, X, will significantly impact the price of rural residential accommodation in Suzhou. However, the p-values of X₂, X₃, and X₄ are all greater than 0.05, so they will not significantly impact on the price of rural residential accommodation. Therefore, the hedonic price model of Suzhou rural homestays is summarized as follows:

$$\ln P=4.813+0.457X_1+0.314X_5$$

(5)

6. Discussion

The interaction of multiple and multi-dimensional factors influences the formation of the spatial differentiation of the price of rural homestays in Suzhou. The formation mechanism of the spatial differentiation of rural residential prices in Suzhou can be analyzed from the macro and micro levels. The first level is macro-regional differences. Regional economic, cultural, social, and other environmental factors affect the spatial differentiation of rural homestay prices:

• The geographical distribution of natural resources and human resources: Rural homestays in Suzhou have unique location characteristics and are densely distributed in the main rural tourist attractions and resorts in Suzhou. These areas have the unique natural and human resources of the countryside. Because the distribution of rural scenic spots and resort areas is not balanced, the spatial distribution of rural homestays shows significant differences. As the third largest freshwater lake in China, Taihu Lake has a unique lake landscape. At the same time, Taihu Lake is a tourist resort with perfect infrastructure. Therefore, the price of rural homestays in the Taihu Lake region far exceeds that of rural homestays in other areas of Suzhou.
• The government’s tourism department has formulated the industrial layout of tourist attractions through urban planning. Suzhou has 11 ancient villages and 15 ancient towns, where many idle houses can be directly used to develop the rural homestay industry, and scenic spots also encourage homestay-related practitioners to rent ancient buildings to operate rural homestay businesses. The operation cost of rural homestays based in ancient towns is low, and there are more ancient towns with similar basic conditions and fierce competition. Hence, rural homestay prices in ancient towns are relatively low, forming a low-price gathering area.
• Spatial transportation status: Suzhou’s public transportation covers the whole city. There are dedicated bus tours and the metro connects the main transport hubs to rural areas. Public transport is cheap, with the cost of public transport from the train station to the leading rural attractions only CNY 5–10. Most rural homestays are easily accessible by public transport. Therefore, the impact of the public transport evaluation index on the price of rural homestays is very insignificant.

The second is the micro differences in individual rural homestays. The prices of homestays in the same location reflect the quality of the products and services they provide. In contrast, the conditions of these products and services are weakly related to tourism resources, regional economy, social culture, and other factors in the region, resulting in significant differences in the prices of homestays in the same spatial background. According to the hedonic price model (Formula (5)), the factors affecting the price of rural homestays are deeply analyzed, and the specific analysis results are as follows:

• The architectural features of Suzhou rural homestays influence the price to the greatest extent, followed by the features of facilities. According to rural homestays’ characteristics and functions, homestays equipped with recreational facilities and outdoor activities are usually more expensive than those without. The material space of rural homestays to meet the needs of tourists, carrying various functions such as cultural inheritance and tourists’ relaxation, requires deep integration of various spatial elements. Suzhou country homestays with local character and high-quality indoor and outdoor Spaces are priced higher per night.
• Tourist attractions have little influence on the price of Suzhou rural homestays. The high-level tourist attractions and the number of scenic spots around Suzhou’s rural homestays have a minimal positive impact on the prices. This phenomenon shows that the level and number of tourist attractions are not the main factors in the pricing of most Suzhou rural homestays. This result is also in line with the main reason tourists choose Suzhou countryside as a travel destination: to have a vacation and leisure, to experience the daily life pattern of Jiangnan water towns, and scenic spots are only secondary travel goals.
• Transportation factors have no significant impact on the price of homestays. The traffic and land in rural areas of Suzhou are not as tight as in urban areas, so it is not important for homestays to have parking lots themselves. Nearby public parking lots and roadside parking spaces can solve the parking problem.

6.1. Theoretical Significance

Against the backdrop of the rapid development of self-driving travel in China, this study added the parking lot factor of internal characteristics. Compared with the previous index system, it further enhanced the model’s explanatory power. It also enriched the theoretical system of the hedonic price model in the accommodation industry.

Furthermore, based on the traditional hedonic price model and the method of GIS spatial data analysis, this study determined through the Moran’s I index that the spatial heterogeneity of rural homestays in Suzhou impacts accommodation prices. Based on exploratory regression, the GWR model was applied to analyze the potential relationship between the price distribution of rural homestays in Suzhou and environmental heterogeneity characteristics, compared with the classic Ordinary Least Squares regression method (OLS). The results show that although the AIC and corrected R2 of the GWR model are superior to those of the OLS model, the gap is not apparent, thus confirming that spatial heterogeneity has little influence on the prices of rural homestays in Suzhou. The research suggests that the GWR model has specific application prospects in the related research of hedonic price models, helping to identify the relationship between the dependent variable price and the explanatory variable in space.

This study further enriches and expands the scope and research objects of the price research on rural homestays. There are specific differences between homestay accommodation in China and that in developing countries worldwide. Many rural communities in developing countries are powerless due to their status, inequality, and lack of welfare. The proportions of tribes, castes, and ethnic minorities are very high. Rural homestays mainly attract tourists through distinctive lifestyles and cultural activities.

Meanwhile, rural homestays also encourage rural communities to have contact with the outside world, broaden their horizons [53], and increase income and community cohesion. As a developing country, rural homestays in China are all located in well-conditioned rural areas (for example, good natural environment, excellent infrastructure, and well-known scenic spots). The behavioral patterns of Chinese consumers have influenced the distribution of rural homestays in China. Chinese consumers prioritize privacy, individuality, and ecological environments when selecting rural homestays, often preferring those in remote rural areas [19] (pp. 1–11). Rural homestays in China reflect more of the nature of resort hotels. This status differs from traditional rural homestays in developed countries such as Europe and America, which aim to save accommodation costs [29] (pp. 43–53).

6.2. Practical Significance

According to the research results, the operators of rural homestays can enhance the market competitiveness of their homestays by improving the quality of facilities in the rooms and public areas. Online ratings also have a positive impact on the prices of rural homestays. Improving service quality in exchange for consumers’ online positive reviews helps increase rural homestays’ economic benefits at a lower cost. It is advisable not to build private parking lots inside rural homestays, as they encroach upon the limited space for courtyard activities.

The research has practical guiding significance for the government’s future site selection of rural homestays. An analysis of the prices of rural homestays at the geographical and spatial level is helpful for local governments to rationally plan the rural homestay industry and optimize the spatial layout of rural homestays within the region. Relevant government departments need to enhance the profitability of rural homestays by attaching importance to environmental protection in rural areas and upgrading the infrastructure in rural areas. High-end rural homestays have an agglomeration effect. When the government selects locations for high-end rural homestays, it should prioritize areas with a good ecological environment and high privacy.

7. Conclusions

Through spatial autocorrelation analysis using ArcGIS software, the study found that the price distribution of country homes in Suzhou showed apparent spatial clustering and homestays with similar pricing tended to be clustered in space. Within a particular spatial range, this spatial aggregation feature is pronounced, which is consistent with the conclusions of other studies [19] (pp. 1–11). For example, rural homestays on Xishan Island in Suzhou are generally priced higher, while those near other popular rural tourist spots are relatively cheaper. This phenomenon is because Xishan Island of Taihu Lake in Suzhou has begun to develop high-end rural homestays, whose prices, facilities, and positioning are much higher than those of rural homestays in other areas of Suzhou, which has a particular relationship with the government’s positioning of Xishan Island of Taihu Lake as a leisure resort. However, several rural homestays still have significantly higher prices in these high-pricing clustered areas. A further study reveals that rural homestay prices positively correlate with the quality of buildings and facilities. The architectural features of rural homestays in Suzhou have the most significant impact on their prices, followed by the facility features. Online ratings also positively impact the prices of rural homestays, while the private parking lots of homestays harm their prices.

Compared with affordable homestays, high-priced homestays in Suzhou choose to upgrade their hardware facilities during the development process. However, this development pattern of rural homestays does not match the behavioral pattern of Chinese consumers who prioritize privacy, individuality, and the ecological environment when consuming high-priced homestays [19] (pp. 1–11). Therefore, the development path of Suzhou high-priced rural homestays is not sustainable. Homestay practitioners in Suzhou should avoid investing too much money in projects such as upgrading rural homestay houses and facilities. It is necessary to adjust the pricing strategy according to the geographical location of rural homestays and enhance the attractiveness of homestays to tourists in their region. At the same time, the accessibility of rural homestays can be improved, for example, by providing free transfers between rural homestays and major public transport stations. Reducing operating costs while ensuring the quality of facilities and services in rural homestays and reducing unnecessary expenses can achieve sustainable development of rural homestays in Suzhou.

In order to further enhance the explanatory power of the model, the selection of research variables can be more diverse. Currently, many rural homestays are managed on a large scale by hotel groups. Future research can include the impact of brands on the prices of rural homestays. As a region with many ancient villages and towns, Suzhou can also introduce elements of historical buildings and decoration styles in future research. In addition, research in the dimension of time can be incorporated. In future research, the geographically and temporally weighted regression (GTWR) model, which introduces the concept of time, can be used further to improve the research on the prices of rural homestays. For example, the pricing factors of rural homestays can be studied based on seasonal changes such as the peak and off-peak seasons of tourism. Perhaps it is better to segment the research on rural homestays based on consumers to improve the accuracy of the interpretation. Similarly, the policy influencing factors among different regions are also worth considering. Research on the impact of the support from relevant government departments in different regions on the prices of rural homestays can be included. Current studies mainly focus on provincial or cross-regional scales, relying on macro methods such as buffer zone analysis and kernel density maps. However, there is still a lack of systematic explorations of spatial differentiation mechanisms at the small-scale community level (such as the neighborhood effect of rural homestays and the role of terrain constraints), which limits the accuracy of differentiated land policies and infrastructure planning.