In this section, we introduce the main empirical findings of this paper from the following three perspectives. First, we compare the differences between home buyers and renters in their demands for various features and amenities, especially urban parks. Then, the community property management service fees and greening rates are used to characterize the two dimensions of community quality. The second part and the third part, respectively, compare the heterogeneous demands of residents and renters for urban parks with the two dimensions of the property management service fee and the greening rate.
4.1. Overall Different Demands between Homebuyers and Renters
The regression results of the HPM based on time and spatial fixed effects are shown in
Table 4, with robust t-statistics in parentheses. Among them, the dependent variable in columns (1)-(3) is the housing price, and the dependent variable in columns (4) and (5) is rent. Both the HPM for housing price and the HPM for rent are controlled by variables of the physical characteristics of housing, community characteristics, and other location amenities. We also introduce “
Dis_park” as the key variable for identifying the demand for urban parks in column (3) and column (6). The
R2 in the HPM for housing price is about 0.76, and that in the HPM for rent is about 0.65, indicating that our model can explain about 76% of the spatial variation in housing prices and 65% of the spatial variation in rent. Overall, the empirical results of the main control variables are in line with expectations.
According to the HPM for housing prices in column (1), among the physical characteristics of housing, variables such as “Age”, “Area”, “Bedroom”, “Livingroom”, “Propertycosts”, “Plotratio”, and “Greenrate” have significant effects on housing prices. Among them, the “Age” and “Area” coefficients are estimated to have negative signs, and “Bedroom” and “Livingroom” coefficients are estimated to be positive. Home buyers prefer new houses with smaller areas and more rooms and living rooms. The plot ratio and green rate reflect the building density and greening of the community, respectively. The plot ratio coefficient is estimated to be −0.0170, and the coefficient of the green rate is estimated to be 0.0168—that is, the higher the plot ratio of the community is, the lower the housing price will be. Conversely, the higher the green rate of the community is, the higher the housing price will be, indicating that residents prefer low building density and residential areas with a better green environment when buying a house. Column (2) shows that the coefficient of “Propertycosts” is 0.0411, and the estimated result of the quadratic term “Propertycosts2” is −0.0022. The relationship between the housing price and the property management service fee has an inverted U-shape and an extreme value of 9.341. When the community’s property management service fee is lower than 9.341 CNY/m2, the housing price increases as the property management service fee increases. When the community’s property management service fee is higher than 9.341 CNY/m2, the higher the property management fee is, the lower the housing price will be. For the other location amenities, the coefficient of “Dis_tam” is estimated to be −0.0170, which is significant at a statistical level of 1%—that is, every 1 km decrease in distance to an employment center will yield a 1.7% increase in housing prices. The employment sub-center also has a greater impact on housing prices: every 1 km from the nearest employment sub-center will yield about a 0.12% increase in housing prices. The coefficients of the “Subway”, “Education”, and “Hospital” variables are estimated to be significantly positive; that is, the proximity to these amenities has a positive impact on housing prices, reflecting home buyers’ significant demands for public transportation, education, and medical amenities.
Renters’ preferences for most features are similar to those of home buyers, but there are differences in their specific preferences. According to column (4), in the physical characteristics of housing, the coefficients of the variables “Bedroom” and “Livingroom” are estimated to be 0.0603 and −0.0346. The impact of the number of living rooms on the rent is negative. Since most rented houses take the form of shared rents, renters prefer practical bedrooms relative to public living rooms. Like home buyers, the coefficient of “Propertycosts” in the HPM for rent is positive; the estimated coefficient is 0.0563, the estimated coefficient of the quadratic term is −0.0014, and the calculated extreme point is 20.107. Thus, when the property management service fee is greater than 20.107 CNY/m2, the higher the property management service fee is, the higher the rent will be. Among the other location amenities, “Dis_tam” and “Dis_job” have a great impact on rent. Each 1 km of additional distance to an employment center and employment sub-center yields a decrease of 2.37% and 1.29% in rent, respectively. The estimated coefficients of the variables of “Subway”, “Education”, and “Hospital” are 0.0610, 0.0184, and 0.0501, which are significant at a statistical level of 1%, indicating that renters attach importance to public amenities like transportation, education, and medical, especially transportation.
Regarding the demand for urban parks, both home buyers and renters have a demand for parks and care about the distance of the residential area to a park in column (3) and column (6). The coefficient results of “Dis_park” in the HPM for housing price and rent are −0.0253 and −0.0220, respectively, which are significant at a statistical level of 1%, indicating that residents pay attention to the accessibility of the parks around the residential area when buying or renting a house. In comparison, the home buyers’ demands for parks around the community are greater than those of the renters.
In short, the difference between home buyers and renters are reflected in the three physical characteristics of housing, community characteristics, and location amenities. Compared to home buyers, renters care more about private room space and are more sensitive to the location and traffic conditions of the house. Moreover, the demand for parks around the community of home buyers is greater than renters.
4.2. Heterogeneous Demand for Urban Parks by Urban Residents with Different Community Traits
This section divides the community into two primary dimensions and analyzes the different demands of home buyers and renters for urban parks based on different community traits. One is from the perspective of community property management service fees, to analyze the difference between the demands of urban residents for urban parks in communities with high and low property management service fees; the second is from the perspective of the internal greening rate of the community, to compare the differences between the demands of urban residents for urban parks in communities with high and low greening rates.
4.2.1. Heterogeneous Demand for Urban Parks by Urban Residents in Communities with Different Property Management Service Fees
The regression results for the heterogeneous demand for urban parks by urban residents in communities with different levels of property management service fees are shown in
Table 5, with robust t-statistics in parentheses. Columns (1) and (2) apply to home buyers, and columns (3) and (4) are used for renters. The
R2 indicates that our model can explain about 76% and 62% of the spatial variation in housing prices and rents. According to the comparative analysis of the regression results, we can draw the following conclusions.
Home buyers living in communities with high levels of property management costs will pay less attention to the urban parks around the community. Column (1) estimates whether there are parks within 500 m of the community with “Park” and whether high property management service fees (“PM”) affect the housing prices. The results show that a park within 500 m of a community can yield a 0.67% increase in housing prices. If the community has a property management service fee, and that property management service fee is high, housing prices can increase by 5.65%. In column (2), the interaction term variable “PM × Park”, which characterizes the impact of a higher community property management service fee on parks around the community, has an estimated coefficient of −0.0307, which is statistically significant at the 1% level. This shows that the home buyers in communities with low property management service fees have higher demands for urban parks than those in communities with high property management service fees.
Contrary to the preferences of home buyers, renters who live in communities with high property management service fees have a positive demand for parks around the community. From column (3), we can see that when there is a park within 500 m of the community, the rent will increase by 1.62%, and when the property management service fee in the community is high, the rent will increase by 3.23%. In column (4), the estimated coefficient of the interaction term “PM × Park” between “Park” and “PM” is 0.013, which is statistically significant at the 1% level. The results show that the house renters in communities with high property management service fees have higher demands for urban parks than those in communities with low property management service fees.
4.2.2. Heterogeneous Demands for Urban Parks by Urban Residents in Communities with Different Greening Rates
The regression results for the heterogeneous demand for urban parks by urban residents in communities with high and low greening rates are shown in
Table 6, with robust t-statistics in parentheses. Here, the dependent variable in column (1) and column (2) is housing price and the dependent variable in column (3) and column (4) is rent. The
R2 indicates that our model can explain about 76% and 65% of the spatial variation in housing prices and rents.
According to the comparative analysis of the regression results, we can see that home buyers living in communities with high greening rates will pay less attention to the urban parks around the community. Column (1) estimates whether there are parks within 500 m of the community (“Park”) and whether a high greening rate (“Green”) affects the housing prices. A high greening rate in a community can increase housing prices by 1.74%. In column (2), the interaction term variable “Green*Park”, which characterizes the impact of a higher community greening rate on parks around the community, has an estimated coefficient of −0.0269, which is statistically significant at the 1% level. This shows that the home buyers in communities with low greening rates have higher demands for urban parks than those in the communities with high greening rates.
However, renters who live in communities with high greening rates have a positive demand for parks around the community (different from the preferences of home buyers). In column (3), we can see that when there is a park within 500 m of the community, the rent will increase by 1.39%, and when the property management service fee in the community is high, the rent will increase by 2.61%. In column (4), the estimated coefficient of the interaction term “Green* Park” between “Park” and “Green” is 0.0376, which is statistically significant at the 1% level. This indicates that the house renters in communities with high greening rates have higher demands for urban parks than those in communities with low greening rates.
In this section, we verify the hypotheses of the heterogeneous demand for urban parks by home buyers and renters with different community traits. The home buyers in communities with high property management service fees and greening rate levels have lower demands for urban parks than those in communities with low property management service fees and greening rate levels, while the house renters in communities with high property management service fees and greening rate levels have higher demands for urban parks than those in communities with low property management service fees and greening rate levels.