1. Tutorial on Local Polynomial Regression (LPR): An Alternative to Ordinary Lease Squares by John M. Clapp March 10, 2000 I. Motivation: What LPR does. II. How LPR works with OLS: An Overview. III. Application to Housing Transactions in Contra Costa County, California. IV. Technical aspects of LPR, with Equations. V. Full color outline, Figures and Equations are available at: http://www.sba.uconn.edu/users/johnc/index.htm

2. I. Motivation: What LPR does. n Non-normal data: E.g., Estimating an empirical probability density function or CDF. LPR makes efficient use of scarce data in the tails of the distribution. n Estimate non-linear functions such as price indices, and logistic functions. n See Next 2 slides.

6. IIb. Overview of the Local Polynomial Regression Model n LPR is weighted OLS; LPR uses a polynomial eq. n A weight is applied to each observation in the sample. The “kernel” weighting function is a probability density function. n Let X = Latitude, Longitude and linear time; x is a particular point in space and time. The LPR Eq’s:

7. III. The Data from Contra Costa County n San Francisco, Eastern Suburbs n Rapidly growing area that contains a lot of open space. n All single family housing transactions, 1994 through 1997, 48 months of data. n Data include sales price, date of sale, zip code, and housing characteristics (square footage, etc.) n I added latitude and longitude of the zip code.