Forecasting House Prices with Hedonic Models and Repeat-Sales Indices
Summary
The document considers how to estimate a house’s current value from property characteristics and an earlier sale date. It contrasts including date directly in a gradient-boosting model with first adjusting historical prices for market inflation, then modeling property features. The answer describes a hedonic regression with time effects and identifies the central difficulty: predicting beyond the latest sale dates in the training data.
One proposed workflow estimates a house price index for a reference property, projects the index forward, and uses a hedonic model to tailor the estimate to a particular home. Another replaces the model’s time dummy with a regional repeat-sales index, such as the FHFA index, for the sale date. The index may be projected using serial correlation and seasonal effects; the answer notes that short-horizon projections may be more reasonable, while index data can lag current conditions. The text offers modeling approaches but no comparative validation or detailed forecast specification, so accuracy depends on data coverage, geography, and the quality of the index projection.
Key ideas
- A hedonic model can estimate property value from characteristics while accounting for sale timing.
- Forecasting beyond the latest observed sale dates requires a separate assumption about house price movement.
- A projected reference-house price index can be combined with a hedonic model to value a specific property.
- A regional repeat-sales index can serve as the time component, with serial correlation and seasonality used for short-term projection.
- Index lag and limited validation constrain confidence in estimates near the present.
Tags
Full text
# House price inflation modelling # House price inflation modelling I have a data set of house prices and their corresponding features (rooms, meter squared, etc). An additional feature is the sold date of the house. The aim is to create a model that can estimate the price of a house as if it was sold today. For example a house with a specific set of features (5 rooms, 100 meters squared) and today's date (28-1-2020), what would it sell for? Time is an important component, because prices increase (inflate over time). I am struggling to find a way to incorporate the sold date as a feature in the gradient boosting model. I think there are a number of approaches: - Convert the data into an integer, and include it directly in the model as a feature. - Create a separate model for modelling the house price development over time. Let's think of this as some kind of an AR(1) model. I could then adjust all observations for inflation, so that we would get an inflation adjusted price for today. These inflation adjusted prices would be trained on the feature set. What are your thoughts on these two options? Are there any alternative methods? ## Answer by Sharad (score 2, accepted) https://quant.stackexchange.com/a/60781 The standard approach here (as you probably know) would be to estimate a hedonic regression with a time-dummy. However, the problem you're facing (if I understand it correctly) is to estimate the price for a house with a given bundle of characteristics for times that lie beyond the last sold dates in the data set. One approach you can take is to estimate a house price index for a reference house (assuming you have enough data), project it forward, and then use the hedonic regression to customize the estimate for a specific house. The forward projection of the index is, of course, quite challenging. However, at least locally, house prices do show significant serial correlation so projecting one to two quarters ahead should give reasonable results. I think what I've said above effectively combines your (1) and (2). Another approach would be to take out the time dummy in your model and substitute it with the appropriate value of a repeat-sales house price index (HPI) for that geographic region as of the sold date. Fortunately, there is a high-quality free repeat sales HPI available FHFA House Price Index. The FHFA HPIs are updated frequently although the data is somewhat lagged (by 1-2 months). Once again, a simple time-series model that incorporates serial correlation and seasonal effects should allow you to estimate values for times close to today's dates reasonably effectively.
Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.