A bibliometric analysis of six decades of academic research on housing prices

Abstract

Purpose This paper aims to provide a comprehensive bibliometric study of housing prices according to the articles collected by the Web of Science (WOS). Design/methodology/approach This paper studies 4,125 research papers on housing prices in the core collection database of WOS. Using VOSviewer, this paper makes a bibliometric and visual analysis of the housing prices research from 1960 to 2020 and probes into the housing prices research from five aspects: time, international cooperation, institutions author cooperation and research focuses. Findings Keywords such as influencing factors of housing prices, analysis of supply and demand, policy and housing prices and regional cities appear frequently, which indicates the main direction of housing price research literature. Recent common keywords include regression analysis and house price forecast. Countries, like the USA started early in the study of housing prices, and the means and methods in the field of housing price research are mature, leading the forefront of housing price research. Compared with the USA and other Western developed countries, the housing price research in developing countries needs to use innovative research methods and put more effort on sustainability. Research shows that housing price is closely related to economy, and keyword cluster analysis shows that gross domestic product, interest rate, currency and other keywords related to economy are of high-frequency. Research limitations/implications This paper only uses articles from one database (WOS), which does not represent all research papers published worldwide. Some studies have been published for a long time, and the reference value to the research focuses and future research might be limited. There are many kinds of journals included in the study with different publishing frequencies, time ranges and numbers of papers. These may have some influence on the research results. Originality/value The main theoretical contribution of this paper is to supplement the current academic research on housing prices. This paper reveals the key points of housing prices research and possible research problems that need attention. We can know from the future research direction and practice which can offer insights for future innovative direction.

Using Co-Ordinate Systems in Hedonic Housing Regressions

“…Ross et al [14] assume the coefficient of x is 2, x is an N(0, 1) random variable, and the error term is an N(0, 2) random variable. As previously noted, the true locations of A and B are (0, 15) and (10,15), respectively. The true values of the model parameters are β 0 = 1, β 1 = 2, γ 1 = −0.25, and γ 2 = −0.1.…”

“…In the two subsections below, we describe some recent studies utilizing one or the other approach. The comprehensive bibliometric analysis by Li and Li [15] lists additional studies that fit one or the other category. Lastly, some of the studies reviewed in this section make use of spatial econometrics techniques.…”

“…The landscape features two true landmarks, A and B, both located in the periphery. Their co-ordinates are A = (0, 15) and B = (10,15), both unknown to the researcher. Instead, random landmarks are chosen from the periphery, and distances to each are used in the estimation.…”

“…Recall that for the Ross et al [14] Monte Carlo Simulation I, house locations are randomly chosen from a neighborhood square of length 20 centered at the origin. The locations of the true landmarks in their Simulation I are A = (0, 15) and B = (10,15). The most distant location from points A and B inside the neighborhood is at the point (−10, −10).…”

Network analysis of comovements among newly-built residential house price indices of seventy Chinese cities

“…For economic analysis regarding the real estate sector, many efforts have been devoted to research on price relations (Li and Li, 2022; Hosseini et al , 2022). Fundamental time-series models, such as the VAR and VECM, and different varieties of their variations have been applied widely to perform such analysis (Vishwakarma, 2021; Ji and Bhandari, 2022; Squires et al , 2022).…”