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”
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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
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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
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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
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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
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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
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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
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