Dwelling Price Ranking versus Socioeconomic Clustering: Possibility of Imputation

Abstract

In order to characterize the socioeconomic profile of various geographic units, it is common practice to use aggregated indices. However, the process of calculating such indices requires a wide variety of variables from various data sources available concurrently. Using a number of administrative databases for 2001 and 2003, this study examines the question of whether dwelling prices in a given locality can serve as a proxy for its socioeconomic level. Based on statistical and geographic criteria, we developed a Dwelling Price Ranking (DPR) methodology. Our findings show that the DPR can serve as a good approximation for the socioeconomic cluster (SEC) calculated by the Israel Central Bureau of Statistics for years when the required data was available. As opposed to the SEC, the suggested DPR indicator can easily be calculated, thus ensuring a continuum of socioeconomic index series. Both parametric and nonparametric statistical analyses have been carried out in order to examine the additional social, demographic, location, crime and security effects that are exogenous to SEC. Complementary analysis on recently published SEC series for 2006 and 2008 show that our conclusions remain valid. The proposed methodology and the obtained findings may be applicable for different statistical purposes in other countries which possess dwelling transactions data.

Accuracy of Households’ Dwelling Valuations, Housing Demand and Mortgage Decisions: Israeli Case

“…As expected, the analysis showed that the dwelling area variable explained most of the variance of the dependent variable (Ln price per square metre), consistent with the findings of previous research (Fleishman and Gubman 2015 ). Regarding the effect of the year of construction of a residential building on property value, the model showed an upward trend in the regression coefficients over the years.…”

“…UK, Australia, New Zealand) to use aggregated indices with the purpose of characterizing and documenting the socioeconomic profile of various geographical units (Burck and Tsibel 2013 ). The positive effect of socioeconomic profile of CT on property value (Table 3 ) reflects the well-known correlation between property prices and various effects reflecting the socioeconomic characteristics of the population in a given area (Des Rosiers et al 2002 ; Reed 2013 ; Fleishamn et al 2015 ).…”

The effect of school performance on property values: a literature review and a case study

“…Locality size (in terms of number of residents) has a positive effect on housing prices, as expected. These findings are in line with the results reported in other studies recently conducted in Israel (Fleishman and Gubman, 2015a,b). In addition, a significant negative relation is observed between the percentages of households with children aged 4-12 among the homebuyers and the average house price per square meter.…”

“…Similar to variables reflected socioeconomic characteristics of students and their parents, it should be noted that Israelis generally, and Israeli immigrants specifically, display a wide spectrum of ethnic patterns that are of a great importance not only in the socioeconomic divide in Israeli society, but also in spatial segregation. Moreover, recent study conducted in Israel by Fleishman and Gubman (2015a) shows that the percentage of adult immigrants from Asian and African countries as well as from the former USSR, among neighborhood’s residents, is negatively correlated with housing prices. According to this evidence and following the relevant literature that has been referred in Section 2.2, such characteristics as parents’ origin and education level are expected to influence house prices and are thus included in the models.…”

“…A relation between size of homebuyers’ families and the marginal value of each additional square meter in large dwellings may explain this finding. On the one hand, it stands to reason that families with children aged 4-12 are usually larger than others and therefore buy larger dwellings; conversely, the marginal value of an additional square meter in large dwellings declines steadily (Fleishman and Gubman, 2015a). Consequently, the larger a homebuyer’s family is, the larger the dwelling it buys, making the average price per square meter lower.…”