2008 ESRI Business GIS Summit Spatial Analysis for Business 2008 Program

Transcription

1 A GIS Framework F k to t Forecast F t Residential Home Prices By Mak Kaboudan and Avijit Sarkar University of Redlands School of Business 2008 ESRI Business GIS Summit Spatial Analysis for Business 2008 Program 1

2 Presentation Sequence Objective Review of current home-price models Proposed modeling strategy The GIS framework Methodology Results Conclusion

3 Objective Predict 2006 residential home prices from 4 cities in different counties in Southern California using quarterly historical i data.

4 Review of Current Models Hedonic models Here the price of a house is mainly dependent on houses attributes, Ball (1973). Attributes include (i) building square footage, (ii) number of bedrooms, (iii) number of bathrooms, (iv) age of the house, (v) lot square footage, etc Hedonic models with spatial correlation Using GIS helped add spatial attributes, Can (1998). Spatial attributes include (i) distance to schools, (ii) distance to parks, (iii) distance to city business center, (iv) neighborhood ethnic mix, (v) neighborhood median or average family income, etc. Hedonic submarket models Here a different price equation is estimated for each market segment, Goodman and Thibodeau (2003). Hedonic models using GWR In these models estimated coefficients are not random. They are deterministic functions of location in space. A small amount of bias characterize the estimated coefficients (Fotheringham et al., 2002, p. 52).

5 Common Features Common features of existing statistical modeling methods: They all utilize individual property prices. Their main concern is with producing parsimonious equation estimates. Most predict prices of homes sold during the same estimation period, thus missing temporal changes.

6 Proposed Modeling Strategy Divide each city into neighborhoods. Calculate periodic (quarterly) average neighborhood prices. Detect t problems of heteroscedasticity ti it and/or autocorrelation. Resolve statistical problems associated with panel data. Produce different models of neighborhood averages and select the one that delivers the best forecast.

7 The GIS Framework Defining i Neighborhood h Resolutions

8 Description of Data 4 So. Cal. counties San Bernardino Riverside Los Angeles San Diego 1 city in each county County San Bernardino Riverside Los Angeles San Diego City Redlands (RL) Riverside (RS) Burbank (BB) Carlsbad (CB) historical housing sales data

9 Problem Scale and Representation Individual id housing sales are aggregated into clusters At what level of scale should problem be addressed? How to divide a city s housing market into sub-markets? Hedonic submarket literature commonly uses Zip code Census tract School district Criteria used in this study APN first 3 or 4 digitsit Census Tracts ZIP+1

10 Geocoding Results for APNs Redlands

11 Redlands Neighborhoods Neighborhoods are contiguous non-intersecting polygons (a) CT Neighborhoods (b) APN Neighborhoods (c) ZIP+1 Neighborhoods

12 House Attribute & Spatial Variables Description/Variables Mailing Address City Values 514 S Mariposa St Burbank Ca Burbank APN 244 ZIP Parcel Number 244 CT Sale Date Bathrooms 3.00 Bedrooms 3.00 Garages Lot Square Footage Price Structure Square Footage Construction Age Dist to nearest SCH Dist to nearest HOSP Dist to nearest RECR Dist to nearest GOLF Distance_NATLPARK Distance_STPARK Jordan Middle School PW 0.80 Alameda PB Care Center PA 0.08 PH 0.10 MHHI 57,241 LA Zoo Owner Present/Absent 1.00 YEAR BUILT 1961 Lakeside Golf Club Variable values obtained using standard proximity functions within ArcToolbox

13 Methodology Estimate OLS model. Test for heteroscedasticity using ARCH test (Engle, 1982). Correct for heteroscedasticity and re-estimate models using a generalized least squares method (GLS), Wooldridge (2003). Test for autocorrelation using the runs or Geary test, Geary (1970) Correct for autocorrelation using a feasible GLS, Gujarati (1995).

14 Outcomes Three GLS equations (representing CT, APN, & ZIP+1) were estimated for each of the cities. One GLS equation was estimated using all available prices for each city (ALL). It was possible to correct for all heteroscedasticity ti it problems. Some autocorrelation problems persisted.

15 Variables in Best Equations List of explanatory variables in order of importance SSF AHI RMR2 RMR6 RMR3 LSF RMR5 CA NG SCD PH PB PA ALL BB CT PN ZIP ALL CB CT PN ZIP ALL RL CT PN ZIP ALL RS CT PN ZIP

16 Estimation Results BB CB RL RS ALL CT PN ZIP ALL CT PN ZIP ALL CT PN ZIP ALL CT PN ZIP Obs R MAPE

17 Forecast Results 2005 Forecast Comparison PMA PE BB CB RS RL BB CB RL RS BB CB RL RS BB CB RL RS 0.00 ALL CT PN ZIP

18 Comparative Results PMAPE results in the literature Obs. PMAPE PMAPE results from this study Gençay and Yang (1996) Average CT = Gençay and Yang (1996) Average PN = 8.29 Fletcher et al. (2000) Average ZIP = 9.04 Bourassa et al. (2003) Average ALL in the literature = 15.9 Average ALL = 16.5

19 Riverside APN 2005 Forecast RP Forecast $' Quarter

20 Riverside PN 2006 Prices for Sales in 2005 F-2005 F-2006 F $' Quarter

21 2006 over 2005 Expected % Price Changes 2006 % change over same quarter prices of Q1 Q2 Q3 Q BB CB RL RS

22 Conclusion Using average neighborhood prices helps reduce heteroscedasticity and autocorrelation problems and therefore produces better forecasts of residential housing prices. Parcel number resolution seems to be a promising resolution to use. Further research in this direction is clearly warranted before reaching a definitive conclusion.

23 CSI Fitted Forecast Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09

24 Y = (SIN(COS(x60)) * (((x60 * (COS(x60) * (COS(x60) * ((COS(x60) * ((COS(x60) * (COS((x7 x34)) SIN((x34 + x7)))) * x7)) SIN((x32 + x7)))))) SIN(SIN(x61))) SIN((x32 + x7)))) X7 = Conventional Mortgage Home Price Index (t-18) X32 = Number of houses for sale in thousands (t-19) X34 = Number of houses for sale in thousands (t-22) X60 = Change in housing inventory (t-24) X61 = Excess supply dummy variable