Nanjing is a megacity in Jiangsu province, which has comprehensive and high-quality educational and medical facilities. The population of Nanjing is growing rapidly. Due to this growth, Nanjing has also suffered greatly from the problem of high housing prices in recent years. In 2006, the average housing price in Nanjing was only 5304 CNY/m2
]. In 2018, this value increased to 30,212 CNY/m2
), while the average housing price in China is only 8736 CNY/m2
]. However, housing rent does not show a strong cointegration relationship with house prices, which has remained at a relatively stable level. By contrast, the average housing rent has increased 3.01% per year from 2006 to 2018. This rate approximately equals China’s 1-year bond yield (risk free rate) [3
]. Thus, the average housing rent in Nanjing has maintained a stable level in recent years. Consequently, one cannot use Nanjing’s housing prices to identify the utility value of houses due to the excessively rising house prices. Instead, housing rent, which has escaped the capital bubble, is better able to reflect the utility value of residential houses.
Some studies have explored the residential market by using the Wheaton-Di Pasquale model, which considers housing price and rent as different value performance modes of the residential market [4
]. There has been a number of models and methods adopted to explore the relationship between housing prices and utility value [5
]. Among them, ordinary least square (OLS) regression is the first and the most commonly employed [7
]. Rosen first used the hedonic price model to estimate the value of attributes in goods [11
]. The hedonic pricing model (HPM) considers housing prices to be comprised of three types of independent characteristics: neighborhood, location, and structural attributes. However, the traditional hedonic price model regards its influencing factors as spatially stable, homogeneous, and independent and does not consider the possible spatial differences between some influencing factors.
Another common model is the geographically weighted regression (GWR) model, which has been gradually applied to housing price analysis in recent years [12
]. The GWR model considers that all variables in the model possess the characteristics of spatial non-stationarity, spatial heterogeneity, and spatial dependence. However, the influencing factors do not necessarily have spatial stationarity or non-stationarity, so the GWR model has its own limitations in defining the explanatory variables of real estate prices [13
]. The mixed geographically weighted regression (MGWR) model adds spatial stationary variables, which contain both global variables and local variables, thereby decreasing the error of the GWR model [14
]. Helbich used the MGWR along with the HPM, GWR, and MGWR to explore the determining factors of Austrian housing prices and proved that the factors affecting housing prices had spatial heterogeneity [15
]. This result means that the determining factors of housing prices have significant differences compared to real estate prices in Austria’s metropolises and in the rest of the region [5
In the past, there have been many studies on the spatial distribution and determining factors of housing prices [16
]. However, studies on housing rent are still lacking, let alone studies that analyze rent by MGWR. Nanjing, suffering from a rapid rise in housing prices, has been selected as a pilot city for housing lease reform. Under such circumstances, the research on its housing leasing market is meaningful and representative. Therefore, this paper takes the main urban area of Nanjing as an example area to analyze and compare the spatial distribution characteristics of housing rent, price, and the price–rent ratio and explore the influencing factors of residential rent by using a spatial econometric model. We also analyze the utility value of house rent. The specific research questions are as follows: (1) What is the distribution of housing rent? Is it same as the housing price? (2) What factors significantly affect rent? (3) What are the spatial variations for the degrees of influence? The results of this study will provide governments with a scientific basis to better formulate policies on the residential rental market and urban planning. In addition, the methodology of this paper can be applied to other metropolitan cities to analyze the utility value of their houses. The results provide a reference for the urban planning decision-making and the layout of urban infrastructure in Nanjing. This study is of significance because it offers a reference for urban planning policy.
The following sections of this article are arranged as follows. Section 2
presents the study area, methodology, data collection, selection of variables, and test of variables. Section 3
compares the different models. Then, the spatially varying relationships between rent and each utility factor are presented. Finally, the study is concluded and discussed in Section 4
This article used the rents in Nanjing to spatially explore the utility value of houses from the perspective of rent. Our study shows that the distribution of housing rent generally has a multi-center group cluster pattern, which is not shared by the housing prices in Nanjing. The goal of this paper was to spatially explore the utility value of houses from the perspective of rent with the MGWR model. To identify the spatial non-stationarity and stationarity of variables, mixed geographically weighted regression was applied to deal with this problem. Area, orientation, and decoration did not show significant non-stationarity and were classified as global variables. Primary and middle schools, commercial centers, subways, expressways, railways, and colleges strongly affected rent, which influences significant degrees of spatial variation.
In terms of traffic conditions, subways affected the housing rent most strongly, especially in urban edge areas. Expressways showed diametrically opposite effects in some areas. Meanwhile, railway lines had an obvious value-reduced effect on surrounding houses. Therefore, how to balance the advantages and disadvantages of traffic facilities is an important issue in the future. Suburban areas should accelerate the construction of public transit and road networks. In the downtown area, road widening and noise reduction measures need to be accounted for.
In terms of the neighborhood environment, commercial centers showed a value-added effect to houses around the city center but had little effect on suburban houses. Schools and colleges are not the foremost considered objects for tenants, which is proven by the fact that that when a residential house has no school district attributes, the utility value of the house will decrease dramatically.
These results provide a reference for the management of the residential leasing market and the layout of urban infrastructure in Nanjing. Administrative departments may ponder the effects of each factor in the formation of urban planning. The factors that have a significant value-reduced effect on housing rent reflect the abundance of this infrastructure, and vice versa. Thus, the local residents’ living circumstances can be effectively ameliorated by improving the numbers and functionality of these infrastructures.
This study proves that the MGWR model is an effective residential utility value analysis method that is superior to the traditional OLS and GWR models. The MGWR model can identify non-stationary variables while controlling the smoothness of global variable parameters, making the results more accurate. However, there are still some limitations in this study. Social and economic factors also affect the housing rent to some extent at the micro scale in the main urban area of Nanjing. Future research should explore the relationship between residential houses and socio-economic attributes to more deeply understand the connotations of residential houses.