Larisa Fleishman Yury Gubman Valuation of dwelling is carried out - - PowerPoint PPT Presentation

Mass Appraisal at the Census Level - Israeli Case

Larisa Fleishman Yury Gubman

Valuation of dwelling is carried out with different methods: Price agreed upon in a sale transaction Owners’ self-reported valuation Expert’s (appraiser's) valuation Valuation modeling

In Israel dwelling market values are available

• nly for properties sold during a certain period

and reported to the Tax Authority The main goals: To develop a methodology for estimating market values of all dwellings in Israel To estimate property value data for every record in the Dwelling and Building Register

Work Stage (1)

Step 1: Estimation of Hedonic Model

• n sale transactions

(Tax Authority, 2011) Step 2: Estimation of Hedonic Model

• n owners’ dwelling valuations

(Household Expenditure Survey, 2011)

Step 3: Comparison of the estimated regression coefficients from the two models

Explanation Model

Dependent variables: (log of) Dwelling Price (log of) Subjective Dwelling Valuation Explanatory variables:

– ‘Asset’- dwelling indicators (size of living space, number of rooms, municipal property tax) – ‘Building’ – building indicators (number of apartments in a building, year of construction) – ‘CT’ - indicators of the census tract in which the dwelling is located (socio-demographic characteristics of residents) – ‘Locality’ - indicators of locality in which the dwelling is located (demographic, environment and location characteristics

ijkl l k j i ijkl

u Locality CT Building Asset ice      

4 3 2 1

) log(Pr     

Explanation Model – main results

Variables Estimates for (ln of): Transactions prices1 Owners’ valuations1 Intercept 12.477* ( ( 0.018 12.485* (0.062) Number of rooms 0.218* (0.003) 0.253* (0.013) Area (square meters) 0.006*(0.0001) 0.006* (0.0005) Interaction: Number of rooms * Area 0.0006*(0.00001)

• 0.0007* (0.0001)

Multistory building (yes / no)

• 0118* (0.006)
• 0.094* (0.018)

Municipal property tax (NIS2 per square meter) 0.001* (0.0001) 0.001* (0.0004) Average income from wages and business per capita in census tract (in 1,000 NIS2) 0.007* (0.0001) 0.004* (0.0002) Population in locality (in 1,000) 0.099* (0.001) 0.088** (0/004) Distance from the city center (km) to CT

• 0.035* (0.0001)
• 0.022* (0.004)

Distance from Tel -Aviv to CT (in 100 km)

• 0.835* (0.014)
• 0.889* (0.045)

Square distance from Tel -Aviv to CT 0.225* (0.005) 0.239* (0.015) CT’s bordering on the Mediterranean sea shore 0.095* (0.007) 0.045*** (0.025) South District

• 0.096* (0.008)
• 0.154* (0.024)

North District

• 0.056* (0.009)
• 0.068* (0.028)

R2 0.73 0.74 Number of observations 39,244 2,570

1-S.E. in brackets 2-New Israeli Shekels *Significant at 1% level; **Significant at 5% level; ***Significant at 10% level

Interim conclusions

Sale transaction prices reflect not only the prices of dwellings that are sold, but closely reflect the price level of the entire inventory of dwellings, given the economic situation and the annual amount of sale transactions The model based on transaction data may be used for mass appraisal of the entire housing stock in a given area and at a certain time

Work Stage (2)

Step 4:

Development of the prediction models

Step 6:

Imputation of an assessed market value for every record in the Dwelling Register

Step 5:

Testing of models’ ability to estimate market value with Accuracy Indices:

Mean/Median Absolute Percentage Error (MAPE / MedAPE)

Choice of Prediction Model

For the full sample of transactions:

Benchmark in literature:

It is common practice to apply a stratification procedure (division into estimation cells) The distribution of the dependent variable over the

• fficially defined six administrative districts was

examined Quantile regression analysis was performed to test the distribution of the estimated coefficients over centiles of the dependent variable 27.73 MAPE 13.21 MedAPE

about 30% MAPE about 20% MedAPE

Analysis of model coefficients stability across centiles

• f distribution of logged price per square meter

Logged Mean Price per Square Meter in CT Logged Area Logged Mean Income in CT Proximity to Tel Aviv

Choice of Prediction Model

Nine estimation cells: – (1) inexpensive dwellings (lowest decile, all districts); – (2) expensive dwellings (five uppermost centiles, all districts); – (3) seven cells are differentiated by district for dwellings remaining after the removal of those in (1) and (2). Prediction model was estimated independently for each cell

Choice of Prediction Model

Accuracy indices received: Division into estimation cells has improved the accuracy of the estimated values The obtained standard deviation was about NIS 16,079 on average, about 1.3 % of the mean value (1,262,668 NIS)

20.12 MAPE 13.21 MedAPE 2.21 Percentile 10 5.65 Percentile 25 22.44 Percentile 75 26.86 Percentile 90

Performance of the proposed method in 2011-2013 The proposed method was applied to additional time periods; the accuracy indices continued to be stable during the addressed periods

MedAPE MAPE Estimated standard deviation Year

12.28 20.12 16,079 NIS 2011 12.06 21.26 14,050 NIS 2012 11.58 19.61 13,616 NIS 2013

Final conclusions

Estimation of dwelling values at the nationwide level provides new statistical data with a high geographic resolution on a range of topics, e.g., behavior of the housing market, economic profile of residential areas, well-being and inequality, to name only a few. Value data at the individual-record level also facilitate estimations for small geographical units and population groups defined by socio- economic and demographic characteristics

larisaf@cbs.gov.il