Dwelling Price Ranking vs. Socio-Economic Ranking: Possibility of Imputation

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1 Dwelling Price Ranking vs. Socio-Economic Ranking: Possibility of Imputation Larisa Fleishman 1 Yury Gubman 2 Aviad Tur-Sinai 3 1 Israeli Central Bureau of Statistics, larisaf@cbs.gov.il 2 Israeli Central Bureau of Statistics and Hebrew University, yuryg@cbs.gov.il 3 Israeli Central Bureau of Statistics and Ben-Gurion University, aviadts@cbs.gov.il Abstract The main purpose of the study is to examine whether dwelling price can serve as an indicator of the socio-economic level of various geographic units, while using the socioeconomic cluster (SEC) calculated by the Central Bureau of Statistics (CBS). The study also examines the effects of demographic and social factors not included in the SEC calculations, on the level of dwelling prices in aggregated geographic units. The study is based on an integrated administrative database. A dwelling price ranking was constructed for the designated estimation areas, which was compared with the socio-economic cluster. The study findings indicate a strong positive correlation between the socioeconomic cluster of the locality/region and dwelling price ranking, with the trends for change over time similar for both indices. An analysis using econometric models makes it possible to identify exogenous factors in the socio-economic index, which have a significant influence. Keywords: housing value, residential area, clustering 1. Introduction The current study has two main goals: 1) to examine if dwelling prices can serve as indicators of the socio-economic level of the various geographic units; 2) to examine the extent of the influence of social and demographic characteristics not included in the SEC calculations, on the level of housing prices in a given geographic area. Based on this analysis, conclusions can be drawn regarding the possibilities for imputation of missing SEC values. To achieve the first goal, geographic areas in Israel were graded according to their dwelling price ranking (DPR), and compared with the SEC. To achieve the second goal, econometric models were estimated Dwelling price in light of socio-economic level characteristics The price of a residential property on the free market reflects the willingness of the purchaser to pay not only for the property itself, but also for a specific residential environment in other words, for social space quality (Reed, 2001). 1

2 Research findings from around the world testify to the range of factors which reflect the socio-economic essence of a residential area, the most important of which are income, education, employment, and the demographic characteristics of the population living in a particular geographic area. Earlier studies show a significant positive link between the three major factors characterizing the residential area s socio-economic level income, education and employment, and the price of dwellings (Heikkila, 1992; Potepan, 1996; Goodman & Thibodeau, 1998; Greenberg, 1999; Des Rosiers et al., 2002; Yates, 2002). Income is considered the primary factor (Ozanne & Thibodeau, 1983; Malpezzi et al., 1998). Those with relatively high incomes choose their residential area in an attempt to avoid neighbors with a low socio-economic status. The popular viewpoint considers social problems, such as crime, drug use, and the neighborhood s economic decline resulting in neglected buildings as all being directly linked to neighborhoods characterized by a high proportion of unemployed, and low levels of education and income (Harris, 1999). The study by Kahn et al. (2001) examined the influence of education as one of the dimensions of the socio-economic level of residents of various neighborhoods in the city of Philadelphia on the price of residential dwellings in those neighborhoods. The study findings show a premium of approximately 21% for dwelling prices, with every 10% rise in the proportion of adults with post-high school education. Aside from the aforementioned factors that characterize the socio-economic space and impact property value, the relevant literature has examined the influence of the demographic characteristics of residents, such as age and marital status, on dwelling prices in that neighborhood (Myers, 1990; Heikkila, 1992). There are studies indicating that ethnic composition and personal security in a residential environment have also entered the circle of players which shape the socio-economic space and, as a result, the value of dwellings (Thaler, 1978; Dubin & Goodman, 1982; Buck et al., 1991; Kiel & Zabel, 1996; Harris, 1999). For example, Harris (1999) found that dwelling values decline by an average of 16% when the rate of the Afro-American population exceeds 10% in a neighborhood, and ranges between 10%-60%; with a far more dramatic fall in prices linked to an Afro-American population of over 60% of the neighborhood residents. According to this study, the explanation is not necessarily ethnic preference, but is linked to a greater extent to the social problems tied with the socio-economic status of the Afro- American population, which is usually lower. A strong link between the factors expressing the socio-economic structure of the residential area and the price of dwellings, serves as a theoretical base for the premise behind this study: the price of dwellings in a specific geographic unit can serve as an aggregate indicator of the socio-economic level in that unit The socio-economic index and the socio-economic cluster In order to characterize and document the socio-economic profile of various geographic units, it is common practice to use aggregated indices to convey their socio-economic content in the official statistics of various countries (for example, the bureaus of statistics in England, Australia, and New Zealand). A socio-economic index (SEI) has been developed at the CBS based on the following 14 variables: (1) demographic characteristics (dependency ratio, median age, percentage of families with four or more children); (2) education and schooling (percentage of the students studying for a 2

3 bachelor s or higher degree, percentage eligible for a matriculation certificate); (3) standard of living (level of motorization, percentage of new motor vehicles, average income per capita); (4) labor force properties (percentage of job seekers, percentage of salaried workers and self-employed persons earning up to minimum wage, percentage of salaried workers earning more than twice the average salary); (5) support/pension (percentage receiving unemployment benefits, percentage receiving income supplements; percentage receiving old age pensions with income supplements). SEI is calculated by principal component analysis. The local authorities are then divided into 10 SECs, with Cluster 1 including authorities with the lowest socio-economic level, and Cluster 10 including authorities with the highest socio-economic level. It should be stressed that the price of housing is not included in these variables. 2. Data and definitions The study is based on files of dwelling transactions in the housing market in 2001 and 2003, obtained from the Israel Tax Authority. In total, the basic file from 2001 included 60,851 transactions, and the file from 2003 included 57,223 transactions. To ensure the validity and robustness of the study results, outliers were excluded from the variable which served as the basis for creating the DPR the natural logarithm for the price per square meter. Using this variable, we neutralize the effect of apartment size, the main variable which explains differences in dwelling prices, and represent as far as possible the market value of the property at the aggregate level of a given geographic unit. Defining the geographic units for which the DPR was created (henceforth: estimation areas) stemmed from the following reasons: (1) the number of housing units provides the supply of dwellings for sale on the local housing market; (2) there were an sufficient number of transactions to represent the price level in the housing market for that unit (at least 15 in the current study); (3) housing market, as far as possible, is homogeneous with regard to prices; (4) the localities are scattered throughout the expanse in a manner which permits defining them as approximately belonging to a common housing market. With these reasons in mind, some localities were defined as estimation areas in themselves (57 in 2001, and 59 in 2003), while other localities were combined together in aggregated estimation areas (40 and 41, respectively). In order to examine additional factors which can influence dwelling prices in the locality/ estimation area, but were not included in the SEC calculation, the following administrative databases were used: the Population Registry, the Level of Religiosity file, crime database, terror incidents data and spatial information regarding the location of the estimation areas relative to the center of the country. 3. Creating a dwelling price ranking The estimation areas were divided into ten levels according to the median of the (logarithm) price per square meter in the area, and a ranking according to DPR was thus created, alongside the SEC index. The areas where dwelling prices are the lowest were ranked as Level 1 (price ranking = 1), while the areas with the highest dwelling prices were ranked as Level 10 (price ranking = 10). Clustering by median was chosen for 3

4 reasons of robustness. Table 1 presents the degree of correspondence between the two indices for Table 1: The Socio-Economic Cluster vs. the Dwelling Price Ranking, 2003 DPR The Socio-economic Cluster Total Total It should be noted that similar results were obtained in 2001, with Spearman s correlation coefficient of 0.76 for 2001 (based on 97 estimation areas) and 0.78 for 2003 (based on 100 estimation areas). In Table 1, the digit in each square indicates the number of estimation areas with DPR and the SEC as they appear in the rows and columns, respectively. The cases in which both these rankings are identical appear in bold print; there is an exact correspondence between the SEC and the DPR for 21 estimation areas. From the examination results, it can be concluded that the most obvious lack of correspondence between the SEC and the DPR is characteristic for estimation areas where the SEC is low or low-medium, although these areas are few. The gap between the SEC and DPR for most estimation areas (61 out of 100), is within a ±2 range. Regarding areas where the gap between SEC and the DPR is greater than 2, it was found that areas with a DPR higher than the SEC are mainly situated close to the center of the country. Estimation areas where the DPR is lower than the SEC are located in the more peripheral areas. This finding indicates the spatial aspect contained in the correlation between the SEC and DPR. For the estimation areas with a low-medium SEC and medium (3-5), there was mainly a trend of a negative difference (in most cases the DPR was lower than the SEC). However, regarding the estimation areas in which the SEC is medium-high to high (6-9), the DPR is usually higher than the SEC. It can also be seen that the degree of correspondence between the two indices grows with the rise in the SEC scale of the estimation areas. From the close linkage between these two indices, the SEC and DPR, in 2001 and 2003, it emerges that the developments over time in both of these indices correspond, with Spearman s correlation coefficient being An insignificant difference (up to 3) characterizes, in particular, those estimation areas where the DPR was lower than the SEC. Greater differences (5 and 6) relate to the estimation areas where the DPR is higher than the SEC. It can be concluded that the DPR can serve as an indicator of socio-economic level for most geographic areas in Israel. This finding expresses the existence of an endogenous effect between the socio-economic level and dwelling price ranking. Likewise, a gap 4

5 exists between the two indices, and it is therefore reasonable to assume that there are other factors influencing dwelling prices. Table 2: The correlations between DPR and the explanatory variables Variables Correlation Level of Correlation Level of coefficient significance coefficient significance Total population <0.001 Number of terror incidents Rate of injured in terror incidents Rate of killed in terror incidents Percentage of Jewish population < <0.001 Percentage of Orthodox population Percentage of ultra-orthodox population Percentage of immigrants from the <0.001 former USSR since 1990 Percentage of Ethiopian immigrants Rate of cases of crimes of bodily < <0.001 injury* Rate of cases of morality crimes <0.001 Rate of cases of property crimes Rate of cases of crimes against the public order Distance from the border of the Tel Aviv District < <0.001 * Crime rates were calculated per 1,000 residents In order to explain the above gap, several variables that were not included in the SEI were chosen for testing. Table 2 presents the Pearson correlation coefficient between the DPR and the selected variables. Table 2 shows that of all the variables that have a significant correlation coefficient with DPR, there are three variables characterized by a positive correlation with the DPR: the total population in the locality/estimation area, the percentage of the Jewish population, and the rate of cases of property crimes. The correlation between all the other variables and the price ranking is negative. The correlation coefficients between the DPR and the variables characterizing the severity of terror incidents, in the year of reference and the year preceding it, are not significant in either 2001 or Econometric models and findings To estimate the marginal contribution by each of the above factors to the DPR, an analysis was carried out using a regression model. The estimation was performed using a stepwise selection algorithm for choosing the best model in terms of significance. The models were estimated for areas in the Jewish sector only, since the housing market in the Arab sector works under different conditions from those in the Jewish one, and some of the explanatory variables are irrelevant to the Arab sector (such as the percentage of new 5

6 immigrants). Distance from the Tel Aviv District was included with the goal of addressing the peripherality effect, as Tel Aviv is the biggest metropolitan in Israel. Tests for statistical validity of the estimated models were carried out A multinomial logistical model for the dwelling price ranking variable Multinomial regression model was estimated, with the dependent variable being the probability of being in rank i: (1) P Dwelling _ Cluster i) = logit( α + β Z +... β Z + ) i = ( = i 1 1 k k ε with α, i 2,.., 10 being the intercepts of the model for rank values 2,,10 respectively, where Rank 1 was chosen to be the basis. In (1), coefficients to be estimated, random noise with zero expected value and variance β, j =,..., k denote the regressive j 1 Z j, j = 1,..., k - the set of explanatory variables and ε - 2 σ. Table 3: Multinomial Models for the Dwelling Price Ranking Variable Estimate p-value Odds Ratio Estimate p-value Odds Ratio Intercept for ranking = < Intercept for ranking = < Intercept for ranking = < Intercept for ranking = Intercept for ranking = Intercept for ranking = Intercept for ranking = Intercept for ranking = Intercept for ranking = SEC < Rate of killed in terror incidents Rate of injured in terror incidents Percentage of Orthodox < population (not ultra-orthodox) Percentage of ultra-orthodox < population Percentage of Ethiopian < < immigrants Percentage of immigrants from the former USSR since 1990 Distance from Tel Aviv < < Distance from Tel Aviv < < quadratic function Total population in the < estimation area, in tens of thousands Number of observations Percent Concordant

7 Table 3 presents the final estimated models for 2001 and 2003, with variables which showed a significant effect for at least one of the years. It can be seen that there is a positive correlation between the SEC and the probability of appearing in a higher DPR, given all the other controlled variables. However, it was found that this influence is partially offset as a result of the effect of percentage of the Orthodox population, percentage of new immigrants from Ethiopia, and percentage of immigrants from the former USSR. In addition, the effect of the distance from Tel Aviv is negative. One possible explanation is the negative relation between the distance from the Tel Aviv and employment accessibility in the residential area. This influence is non-linear: the rate of decline of the effect of distance from the Tel Aviv becoming increasingly smaller, the greater the distance from it. It would seem that this finding reflects the constantly decreasing importance of the distance between periphery localities and Tel Aviv as a commuting distance, and the ever-increasing influence of other employment centers situated in the various periphery towns and cities. It was found that the percentage of the ultra-orthodox population in a geographic region has a positive correlation with the dependent variable. This result can be explained by the preference for community residence among this population. A negative correlation between the percentage of the Orthodox population and the DPR in an area is explained, at least in part, by this sector s preference for living in peripheral regions. The influence of terrorism in a residential area was found to be significant and negative during both periods of research. Aside from the factors included in model (1), it can be assumed that an additional explanation for the probability of appearing in a particular DPR is found in macroeconomic variables, such as the regional unemployment rate. At the same time, since the estimation equation was defined for a residential area, these variables cannot be included in the regression models, since they are not calculated for a large proportion of the estimation areas as defined in this study. Table 4: OLS models on logarithm of the median dwelling prices in the area Variable Estimate p-value Estimate p-value Intercept < < SEC < < Rate of killed in terror incidents Percentage of Orthodox population < < (not ultra-orthodox) Percentage of ultra-orthodox < population Percentage of Ethiopian immigrants < Distance from Tel Aviv < < Distance from Tel Aviv quadratic < < function Total population in the estimation area, in tens of thousands < Number of observations Adjusted R

8 4.2. OLS model on logarithm of the median price per square meter With the goal of validating the results of model (1), a regression model was estimated for the natural logarithm of the median price per square meter in the estimation area variable which served for constructing the DPR. The dependent variable is continuous and approximately normal, justifying use of the OLS model with the same set of independent variables as in 4.1. Table 4 shows that, as expected, a positive correlation exists between the socio-economic level and the dwelling prices. The average elasticity of the dwelling price levels relative to the SEC is in 2001 and in This result shows that the socio-economic level made a greater contribution to housing price levels in 2003 than in This finding can be explained by macroeconomic influences emanating from the world financial recovery, and the security crisis in Israel coming to an end during that period. The findings regarding the other explanatory variables are similar to those in the previous model Regression Tree Analysis To examine the factors influencing the dynamics of DPR during , a nonparametric regression tree was constructed, with the dependent variable being the difference between the DPR in 2003 and the DPR in The explanatory variables are identical to those in model (2), with the addition of the ratio between the SEC of 2003 and the SEC of The regression tree method is intended for dividing observations into homogeneous groups, according to any dependent variable, relative to several explanatory variables. A detailed description of the regression tree methodology and of the algorithm for clustering is presented in Breiman et al. (1984). In this analysis, there is no need to divide the Jewish sector from the Arab sector because of heterogeneity, as was done in the above models.. ANOVA: P-Value<0.0001; indicates a change between 2001 and 2003 Figure 1: The regression tree for change in DPR between 2001 and

9 As well, the variables which were removed from the regression model due to multicollinearity were also included (percentage of the Jewish population and crime variables). In Figure 1, the height of the lines between the split points shows the reduction in variance in the group as a result of the division described above, while the numerical value in the final leaves shows the average of the dependent variable in those leaves. For the areas where the percentage of the Jewish population increased by more than 1% between 2001 and 2003, the dwelling ranking rose by an average of 2.67 (the leaf furthest to the right). It appears that the dominant factor for a change in the area ranking according to dwelling price is a change in the percentage of the Jewish population within the estimation area, while an increase in the percentage of Jews causes a significant rise in the DPR. There is a correlation between crime data and a change in the DPR: greater number of criminal cases against the public order in 2001, and a rise in the number of files of bodily harm crimes were observed in areas where price rankings fell. Likewise, an increase in the number of ultra-orthodox Jews in an area, given the percentage of the Jewish population and the number of crimes against the public order, has a negative correlation with the DPR. This finding demonstrates that, in the short-term, the entry of a disadvantaged population does indeed lower housing prices. Given the percentage of the Jewish population and the number of crimes against public order, areas with a relatively small number of residents (up to 15,000, in the current study these are aggregated areas) are more vulnerable to a sharp decline in the DPR than larger areas. 5. Conclusions The current study examined the question of whether dwelling prices in a given geographic area can serve as an indicator of its socio-economic level. The study is based on a series of administrative databases covering the entire country. It was found that during the study period (2001 and 2003), there was a strong connection between the area s socio-economic cluster and the value of its dwellings, with the correlation coefficients almost identical for these two years, ranging between 0.7 and 0.8. Therefore, the ranking based on dwelling prices can serve as an indicator of the socio-economic level of most geographic areas in Israel. Likewise, a significant correlation was found between dwelling prices in a particular area and the percentage of those belonging to defined population groups. It was also found that the size of a locality has a positive correlation with the level of the dwelling prices there. It appears that the effect of the distance from the center of Israel s economic activity is negative, as expected. The effect of terrorism in the area was significant and negative during the two periods of research. The probability of a rise in the dwelling price ranking between 2001 and 2003 is positively dependent on the gap in the SEC. These study findings can serve as a methodological basis for completing missing values in the series of socio-economic indices for geographic units in years in which this index is not calculated. Imputations of this kind can serve as an important working tool for the users of socio-economic index, which promises a continuum of the index series. 9

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