Unlike most US cities that underwent urban decentralization or exurban settlement, which required greater reliance on personal cars, city dwellers in Asian countries are more heavily reliant on various forms of mass transit. Results derived from the model can offer insights into the reasons for housing segmentation in Chinese cities, eventually helping to formulate effective urban planning strategies and equitable housing policies. This paper provides an example of employing open access datasets to analyze the determinants of housing prices. This mixed finding is further discussed in relation to community planning strategies in Beijing. In other words, while proximity to certain amenities, such as convenient parking, was positively correlated with housing prices, other amenity variables, such as supermarkets, showed negative correlations. The results showed that the effects of nearby amenities on housing prices are mixed. Using Beijing, China as a case study, we addressed these two issues by (1) collecting residential housing and urban amenity data in terms of Points of Interest (POIs) through web-crawling on open access platforms and (2) eliminating the spatial autocorrelation effect using the Eigenvector Spatial Filtering (ESF) method. The second refers to the spatial autocorrelation effect inherent in the hedonic analysis. The first pertains to difficulty of data collection in regions where geospatial datasets are strictly controlled and limited. These studies, however, are limited by two methodological obstacles that are relatively difficult to overcome. In the past, scholars have employed the hedonic pricing model to quantify the amenity value in relation to structural, locational, and environmental variables. In addition to an economic upturn and housing policies that have potentially fueled the real estate bubble, factors that have contributed to the spatial heterogeneity of housing prices can be dictated by the amenity value in the proximity of communities, such as accessibility to business centers and transportation hubs. On the basis of these remarks, I think you can conclude whether the econometric specification is a "reduced form" or not.The housing market in Chinese metropolises have become inflated significantly over the last decade. Then $\lambda =1$ and so the marginal utility of the characteristic is the same as the marginal monetary willingness-to-pay for that characteristic. If one assumes quasi-linear preferences of the form The hedonic-price econometric estimation is based on actual transactions, and so on data related to prices that consumers have actually agreed to pay.Įstimates $$\partial p / \partial z_i$$ (and/or their binary-characteristic analogues), which is seen to be the ratio of the marginal utility of the characteristic scaled by the marginal utility of income. Note that we have differentiated the price also, since again, this is not "the market price faced by the consumer" but his/her willingness-to-pay. $$\partial U / \partial z_i = \lambda \partial p / \partial z_i, \ \ \ \forall i$$ This is an important departure from the standard consumer theory, because here, $p$ is not market price, but it is the "willingness to pay" of the consumer, decomposed into a function of many characteristics. Where $M$ is the available income and $p(z_1.,z_n)$ is the price of the good, expressed as a function of those same characteristics. Assume for simplicity (as is usually done in the literature, and as is the OP case), that the consumer will only purchase just one unit of good $y$ (one house). Where "$x$" stands for the composite good, and $(z_1.,z_n)$ are the characteristics of good $y$ that are valued by the consumer. In the benchmark hedonic price analysis, we assume a utility function of the general form
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