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construction of Regional house price indexes – The case of Sweden

construction of Regional house price indexes – The case of Sweden. Lars-Erik Eriksson ( Valueguard ) Han-Suck Song (KTH) Jakob Winstrand ( Valueguard ) Mats Wilhelmsson (KTH and Uppsala University). Motivation-Objective. NasdaqOMX house price index Insurance/financial products

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construction of Regional house price indexes – The case of Sweden

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  1. construction of Regional house price indexes – The case of Sweden Lars-Erik Eriksson (Valueguard) Han-Suck Song (KTH) Jakob Winstrand (Valueguard) Mats Wilhelmsson (KTH and Uppsala University)

  2. 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.

  3. 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

  4. 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 • R2, MSE

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

  6. No. of transactions per month

  7. Step 1: The Hedonic Price Equation

  8. Temporal Aggregation Month Year

  9. Step 2: Cluster Analysis C1: Price development C2: Price level C3: Price development 2 year C4: Geographical proximity C5: Geographical proximity + Price development C6: All

  10. Price development and geographical proximity

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

  12. Index – Region 1-5

  13. Index – Region 6-10

  14. Summary statistics

  15. 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

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