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 Yury Gubman Aviad Tur-Sinai Israeli Central Bureau of Statistics

2 The 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 residents socio-demographic characteristics, not included in the socio-economic index calculations, on the level of housing prices in a given geographic area.

3 Outline 1. Socio-economic cluster (SEC) 2. Dwelling price ranking (DPR) 3. DPR as an indicator of SEC 4. Regression model 5. Concluding remarks possibility of imputation

4 The socio-economic index and the socio-economic cluster In order to characterize the socio-economic profile of various geographic units, aggregated indices for their socio-economic level are used in the official statistics in various countries (England, Australia, New Zealand etc.). In Israel, socio-economic index (SEI) is based on the 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 bachelor s or higher degree, percentage eligible for a matriculation certificate);

5 The socio-economic index and the socio-economic cluster (cont.) (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).

6 Socio-economic cluster (SEC) Socio-economic index is calculated by principal component analysis. Using cluster analysis, the local authorities are then divided into 10 SECs. Cluster 1 includes authorities with the lowest socio-economic level, while Cluster 10 includes authorities with the highest socio-economic level. Housing prices are not included in SEC calculations.

7 Dwelling price data 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 includes 60,851 transactions in 2001, and 57,223 transactions in The natural logarithm of the price per square meter is served as the basis for creating of the Dwelling Price Rank (DPR). To ensure validity and robustness of the study results, outliers of this variable were excluded from the calculations.

8 Dwelling price ranking Defining of the geographic units for which the DPR was calculated (estimation areas) are based on the following principles: (1) the number of housing units provides reasonable stock of dwellings for sale on the local housing market; (2) there are a sufficient number of transactions to represent the price level in the housing market for that unit (at least 15 in the current study); (3) local housing market, as far as possible, is homogeneous with regard to prices; (4) small localities within the aggregative estimation area are scattered in a manner which permits defining them as approximately belonging to a common housing market.

9 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 dwelling prices (DPR) was thus created. The areas with the lowest dwelling prices 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 reasons of robustness.

10 Dwelling Price Rank Dwelling Price Rank DPR as an indicator of SEC 10 9 Socio-Economic Cluster vs Dwelling Price Rank Corr=0.78 )Spearman( Socio-Economic Cluster 10 9 Socio-Economic Cluster vs Dwelling Price Rank Corr=0.76 (Spearman) Socio-Economic Cluster

11 DPR as an indicator of SEC Areas where the DPR is higher than the SEC are mainly located close to the center of the country. Estimation areas where the DPR is lower than the SEC are mostly located in the peripheral areas. This finding indicates the spatial aspect contained in the correlation between the SEC and DPR. Degree of correspondence between the two indices grows with the rise in the SEC of the estimation areas.

12 Percent DPR as an indicator of SEC Changes in SEC and DPR from 2001 to Socio-Economic Cluster Dwelling Price Rank

13 DPR as an indicator of SEC It can be concluded that the DPR can serve as an indicator of socio-economic cluster 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 exists between the two indices, and it is therefore reasonable to assume that there are other factors influencing dwelling prices.

14 Regression model Multinomial regression model was estimated: P( Dwelling _Cluster i ) log it( i SEC Z ) Set of explanatory variables Z includes effects that have not been used in the SEC calculation. Algorithm of stepwise selection was applied. Regression model was estimated for Jewish sector only (due to very small number of estimation areas in the Arab sector).

15 Multinomial logistic regression for Dwelling Price Rank 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

16 Regression model There is a strong significant positive correlation between the SEC and the probability of appearing in a higher DPR. Significant correlation was found between dwelling prices in a particular area and the percentage of those belonging to defined population groups. Size of locality has a positive correlation with the level of the dwelling prices there. 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.

17 Conclusions Dwelling Price Rank can be used as a leading indicator of the socio-economic level of the various geographic units. This study provides a methodological basis for completing missing values in the series of socioeconomic indices/clusters for those geographic units and for those years for which this index is not calculated. Imputations of this kind can serve an important working tool for the users of socio-economic index, which promises a continuum of the index series.

18 Dwelling Price Rank Dwelling Price Rank Recent trends Socio-Economic Cluster vs Dwelling Price Rank (without aggregate estimation areas) 10 9 Corr=0.70 )Spearman( Socio-Economic Cluster 10 Socio-Economic Cluster vs Dwelling Price Rank (without aggregate estimation areas) 9 8 Corr = 0.67 (Spearman) Socio-Economic Cluster

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

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, e-mail: larisaf@cbs.gov.il 2 Israeli