1. CONSTRUCTION OF R EGIONAL HOUSE PRICE INDEXES – T HE CASE OF S WEDEN Lars-Erik Eriksson (Valueguard) Han-Suck Song (KTH) Jakob Winstrand (Valueguard) Mats Wilhelmsson (KTH and Uppsala University)

3. Motivation-Objective NasdaqOMX house price index – Insurance/financial products – Complete market Thin markets – Geographical or temporal aggregation? The objective is to construct house price indexes for all parts of Sweden or at least a large part of the economic value on the single-family housing market.

4. Literature Schwann (1998) – Nearby observation in time Englund et al (1999) – Temporal aggregation – not recommended McMillen (2003) – Nearby observations in space Francke and Vos (2004) – Nearby observations in time and space

5. Research Procedure Estimate hedonic price equation for each region Perform a cluster analysis – Geographical proximity – Price development – Price level – Price development (2 years) – Combination Estimate hedonic price equation for each cluster Evaluate the performance of the models – R 2, MSE

6. Data Single-family houses 2005-2010 Transaction price, contract date, size, quality, coordinates 100 labor markets (93 with transactions) – Based on potential commuting

7. No. of transactions per month

8. Step 1: The Hedonic Price Equation StockholmVästeråsMora Coefficientst-valueCoefficientst-valueCoefficientst-value Living area0.390685.90.456025.90.34766.4 Room 20.07701.6-0.1475-1.80.17341.2 Room 30.14143.0-0.1105-1.40.33342.4 Room 40.18785.9-0.0501-0.60.36712.6 Room 50.20494.3-0.0329-0.40.36713.0 Room 60.21984.6-0.0087-0.10.46693.0 Room 70.22944.80.00100.00.51093.3 Room 80.23775.00.04580.50.43152.5 Room 90.25005.2-0.0243-0.30.31671.6 Room 100.27905.70.03410.40.71063.4 Quality index0.013210.90.058511.70.142710.4 Quality index sq.-0.0001-7.9-0.0008-10.1-0.0021-9.3 Sea front0.382923.60.604914.50.52724.8 Sea view0.091719.10.03622.10.12193.2 Semi-detached-0.0399-9.8-0.0449-3.8-0.0765-1.5 Detached-0.0634-12.4-0.0301-1.9-0.1433-1.4 Building period: -19000.07114.20.04511.20.18061.2 Building period: 1900-39-0.0117-1.5-0.1432-5.2-0.0204-0.3 Building period: 1940-59-0.0755-9.3-0.1442-5.1-0.0984-1.2 Building period: 1960-75-0.0798-10.0-0.1286-4.60.01400.2 Building period: 1976-1990-0.02780.6-0.0229-0.80.11451.4 Building period: 1990-0.00610.60.00340.10.42573.0 Urban-0.0193-4.1-0.0293-1.50.11203.1 R2R2 0.9050.8800.732 No of obs.361584633912 No. of obs/month5026413

9. Temporal Aggregation Month Year

10. Step 2: Cluster Analysis Cluster method No of clusters Average no of observations No of observations in smallest cluster Average R 2 C11218,0366,9090.845 C2924,0569,2790.812 C3924,04912,6040.842 C41415,4616,0660.839 C51119,6718,5770.846 C61021,6464,6660.827 C1: Price development C2: Price level C3: Price development 2 year C4: Geographical proximity C5: Geographical proximity + Price development C6: All

11. Price development and geographical proximity

12. Step 3: Evaluation Constant implicit prices Non-constant implicit prices

14. Index – Region 1-5

15. Index – Region 6-10

16. Summary statistics INDEXR1R2R3R4R5R6R7R8R9R10 average132127120131123121130128123128 std.dev16.2610.398.4813.0710.2510.4313.7613.7911.9611.53 Coeff. of variation 0.12300.08210.07100.09970.08300.08580.10590.10780.09690.0903 RETURN average0.55%0.45%0.36%0.52%0.36%0.22%0.54%0.52%0.47%0.43% std.dev2.73%2.21%3.44%1.99%2.55%5.36%2.75%2.33%2.52%2.70% Coeff. of variation 5.004.979.453.807.1124.215.054.465.356.21

17. Conclusion Thin markets is a problem It is not obvious how solve it Temporal and geographical aggregation has been criticized Especially arbitrary geographical aggregation New method how to aggregate in space based on cluster analysis of regions – Price development and geographical proximity Out-of-sample test Other measures to evaluate the method? Improve the cluster models