Literature Review
The topic of house pricing is very well researched. The housing market is often associated with large cash flows which make sellers and advertisers research the reasons and factors that affect the price of houses and apartments. Beijing as a capital city with a huge market for real estate is an excellent ground for research in the sphere of residential housing prices and factors affecting them. One of the major trends identified in this literature review is the prevalence of hedonic pricing method. According to many researcher groups including Candas, Kalkan, and Yomralioglu (2015), Chen and Jim (2010), He et al. (2010) and others, determining the price of a property by assessing the functional and recreational ‘fertility’ of the nearby territory is one of the most widespread and effective methods in theory and in practice. Another trend is rising value of greenery in proximity to a property on sale. According to Chen and Jim (2010) people there has been more than a 17% increase in the value of attractive landscape among buyers. These and other trends, major findings, arguments, limitations, and strengths of the available research data will be discussed in further detail below.
Key Factors in Forming the Price of Housing
Researchers identify a variety of components of which housing price consists. Among them, the transaction price, distance to center, public transportation options, proximity to various socially significant institutions such as hospitals, schools, kindergartens, the proximity of small and big shopping outlets, etc. Most researchers such as Chen and Jim (2010), He et al. (2010), Randeniya, Ranasinghe, and Amarawickrama (2017) tend to review several factors and compare them to each other to identify the most significant ones. Others such as Ottensmann, Payton, and Man (2008) provide an in-depth study of a single factor or, as in the case with Zheng, Sun, and Wang (2014), focus a heterogeneous group of factors.
On the one hand, comparative studies seem to contribute more to the developing of practical data and forming usable clusters information for agents and buyers to use. On the other hand, this approach does not let assess the reasons and mechanisms that form the significance of the factor to a certain market. It appears that both research approaches can be deemed justifiable as they let further research possible and more evidence-based. The in-depth knowledge of each factor separately contributes to the wiser choice of comparison tactics. For instance, in the works of Xiao et al. (2017) and Ottensmann et al. (2008) explore the value and significance of location as a major element that forms housing price. The latter group of researchers argues that monocentric model provides a wide choice of measuring techniques. For example, for determining the value of location parameter, Ottensmann et al. (2008) discuss travel time to center, travel distance to center, distance to employment, and other procedures. This, as the researchers conclude, helps build more reliable data for comparison of location with other factors (Ottensmann et al., 2008).
The factor that is arguably the most frequently discussed one in scientific circles is location. There are different approaches to its definition and measurement (Ottensmann et al., 2008). Location is a multidimensional and complex factor that, includes several metrics such as accessibility, distance, proximity to viable social objects, etc. It has been found that in multiple cities and countries including Moscow, San Francisco, Los Angeles, Chicago, Beijing, and others there is a strong positive correlation between distance from a city center and price of a housing unit.
The other significant factor that is discussed in the literature is land transaction price (He et al., 2010). According to the results of the application of mathematical modeling tool to different factors, this aspect was considered the most influential as its t-ratio, p-value, and other estimation results were significantly better than those of other factors (He et al., 2010). Another factor identified by the researchers is FAR. FAR is significant as it directly correlates with the quality of living. Densely built areas, according to Cebula (2009), He et al. (2010), and Zheng et al. (2014), offer less open access to public facilities, present waste issues, and other unpleasant items that can lower the price of housing.
Greenery was identified as increasingly significant by Chen and Jim (2010) and Zhang and Dong (2018). The pleasing qualities of the landscape such as proximity to parks, gardens or, at least, street greenery have been long advocated for in urbanist literature. Aesthetic and health-related qualities of the landscape have been found to form up to 10% of the housing price. According to the other data, the landscape component may take up to 17%.
There are also many other aspects that contribute to housing price including scarce land supply, interior properties, exterior construction material, building age, etc. (Cebula, 2009; Zheng et al., 2014). Yet, according to comparative analyses done by He et al. (2010) and Zheng et al. (2014), the results are either outmatched by location, and land transaction or they remain mixed and inconclusive.
Major Issues, Debates, and Limitations in Relation to Housing Price Determinants
There is a certain number of issues related to factors that affect housing prices. Different researchers concentrate on a variety of factors and value them as the most significant. For instance, He et al. (2010) argues that top three aspects of residential housing price are floor area ratio (FAR), land transaction price and distance from the center of a city. According to He et al. (2010), these three factors constitute 98.8% of the price. The authors used a linear and semi-logarithm regression model to prove that. The structural variables showed close correlation, yet certain variables performed better during test-drop. As a result, variables such as a park, transit availability, hospital and others lost significantly to the transaction price, distance from the center, and FAR. This point of view is reinforced by other prominent scientific works. As such, Ottensmann et al. (2008) and Zhang and Dong (2018) suggest that distance to the center is one of the most significant price-forming factors in real estate market.
On the other hand, Cebula (2009) argues that positive effects on the price were demonstrated by variables that pertain mostly to interior design and functionality. Aspects such as the number of bedrooms rooms, fireplace, square footage, etc. were found to be more statistically significant than certain outside amenities such as proximity to bus stops, waterfront, shops, and other public facilities. However, both Cebula (2009) and He et al. (2010) do not use the same variables, so strictly speaking, this is not an argument between the significance of one or the other. Rather it is a clash of variable choice for analysis.
There has also been a debate among scientists as to what should be called a center. Ottensmann et al. (2008) cite researchers who believe that center is primarily a concentration of recreational functions, while Xiao et al. (2017) argue that center or centers are the major concentration of the workplaces. Both researcher groups agree that these functions are often imbued with a single location within the city.
There are certain issues that persist in the literature. Most of the researchers use open-source statistical data on the housing transactions. These often include geospatial datasets, volunteered geographic information, the point of interest data, etc. One other issue is the lack of certain significant data on transactions such as person’s own argument for buying and selling. It is often unknown why people choose to buy one or another house. In addition, the statistical information that is mostly used by the researchers is biased in this regard as the choice of a certain acquisition is not always driven by clear and transparent logic. Therefore, mathematic equations and formulas are not always applicable to such data. Generally, the area of sellers’ individual influence is widely under-researched.
Among other limitations is the lack of comprehensive multi-variable research which utilizes regressive analysis, wavelet-based denoising method, and a range of descriptive statistics. There is a lot of solid research, yet they, arguably, lack scale. No researcher presently has compared and analysed all of the existing variables.
Conclusion
All things considered, the housing prices and variables that affect them are widely studied and discussed in scientific literature. A lot of research has been focused on an in-depth study of certain variables but significantly more has been aimed at a comparison. Most of the researchers use regression analysis and draw data from open geospatial and economic data sources. Despite wide coverage, the research generally stays fragmented. In addition, there is not enough emphasis on self-reported data study.
References
Candas, E., Kalkan, S. B., & Yomralioglu, T. (2015). Determining the factors affecting housing prices. Web.
Cebula, R. J. (2009). The hedonic pricing model applied to the housing market of the city of Savannah and its Savannah historic landmark district. The Review of Regional Studies, 39(1), 9-22.
Chen, W. Y., & Jim, C. Y. (2010). Amenities and disamenities: A hedonic analysis of the heterogeneous urban landscape in Shenzhen (China). Geographical Journal, 176 (3), 227-240. Web.
He, C., Wang, Z., Guo, H., Sheng, H., Zhou, R., & Yang, Y. (2010). Driving forces analysis for residential housing price in Beijing. Procedia Environmental Sciences, 2, 925–936. Web.
Ottensmann, J. R., Payton, S., & Man, J. (2008). Urban location and housing prices within a hedonic model. Journal of Regional Analysis and Policy, 38(1), 19-35.
Randeniya, T., Ranasinghe, G., & Amarawickrama, S. (2017). A model to estimate the implicit values of housing attributes by applying the hedonic pricing method. International Journal of Built Environment and Sustainability, 4(2), 113-120. Web.
Xiao, Y., Chen, X., Li, Q., Yu, X., Chen, J., & Guo, J. (2017). Exploring determinants of housing prices in Beijing: An enhanced hedonic regression with open access POI Data. ISPRS International Journal of Geo-Information, 6(11), 358-366. Web.
Zhang, Y., & Dong, R. (2018). Impacts of street-visible greenery on housing prices: Evidence from a hedonic price model and a massive street view image dataset in Beijing. International Journal of Geo-Information, 7(3), 104-116. Web.
Zheng, S., Sun, W., & Wang, R. (2014). Land supply and capitalization of public goods in housing prices: evidence from Beijing: Land supply and capitalization of public goods. Journal of Regional Science, 54(4), 550–568. Web.