Workshop on Residential Property Price Indices RPPI Handbook Chapter 6: Hedonic Regression Methods
Contents • 6. 1 Introduction • 6. 2 Time dummy variable method • 6. 3 Characteristics prices or imputation methods • 6. 4 Stratified hedonic indexes • 6. 5 Main advantages and disadvantages • 6. 6 – 6. 8 Applications to Dutch town of “A” Chapter 6 1
6. 1 Introduction • Assumption: house is a bundle of (price determining) characteristics • Different functional forms possible • Typically estimated by least squares regression • More flexible if characteristics parameters are allowed to change Chapter 6 2
6. 2 Time dummy variable method • Time dummies capture price change • Usually: logarithmic-linear model • Resulting price index has a geometric structure • Revisions when new data is added Chapter 6 3
6. 3 Characteristics prices / imputation methods • Uses traditional index number formulae for a ‘standardized’ house • Are imputation indexes (with quantities equal to 1) • Linear and log-linear model specifications possible • As with other methods: data cleaning may be necessary Chapter 6 4
6. 4 Stratified hedonic indexes • Stratification highly recommended … • as different sets of characteristics will be needed for different market segments, and • parameter values may differ across segments • Sales RPPI: stratified Fisher hedonic imputation index probably the best solution Chapter 6 5
6. 5 Main advantages and disadvantages • Can adjust for both sample mix and quality changes (data permitting) • Probably the most efficient method • Imputation variant is analogous to matchedmodel methodology • Data intensive • General idea easily understood but some technicalities not so easy to explain Chapter 6 6
6. 6 – 6. 8 Application to Dutch town of “A” • Three variables: land size, structure size and age • Models with quality adjusted structures (net depreciation rate, depending on age) • Including linear time dummy model • Fisher hedonic imputation index is “best” very similar to (non-hedonic) stratified Fisher index Chapter 6 7
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