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Splines models for prediction of house prices

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  • David Boniface

    (Epidemiology and Public Health, University College London)

Abstract

Aim: To create a web-based facility for customers to enter an address of a house and obtain a graph showing the trend of price of house since last sold, extrapolated to current date, within milliseconds. Method: The UK Land Registry of house sale prices was used to estimate mean price trends from 2000 to 2010 for each category of house. The Stata ado-file uvrs (with user-specified knots) was used to model the curve. The parameter estimates were saved. Later, to respond in real time to a query about a particular house, splinegen was used to generate the spline curve for the appropriate time period, which was adjusted to apply to the particular house and plotted on the webpage. Challenges: use of coded date, choice of user knots for splines, saving and retrieving the knots and parameter estimates, use of log scale for prices to deal with skewed price distribution, estimation of prediction intervals, and the 2009 slump in house prices

Suggested Citation

  • David Boniface, 2011. "Splines models for prediction of house prices," United Kingdom Stata Users' Group Meetings 2011 09, Stata Users Group.
  • Handle: RePEc:boc:usug11:09
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