1. Workshop on Residential Property Price Indices
RPPI Handbook
Chapter 6: Hedonic Regression Methods

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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 matched-model methodology
Data intensive
General idea easily understood but some technicalities not so easy to explain
Chapter 6

8. 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