1. Sam Copeland July 31st, 2007 GISC 6387
Census vs. Appraisal
Sam Copeland
July 31st, 2007
GISC 6387

2. Objective and Hypothesis
Objectives
To determine what differences if any exist between the US Census records of housing value and the Dallas County Appraisal District’s records of housing value.
To analyze and explain any differences that occur.
Hypothesis
There may be large discrepancies between individual records, but the overall datasets should be relatively comparable.

3. Existing Literature Two key articles
Kish, L; Lansing, J. Response Errors in Estimating the Value of Homes. Journal of the American Statistical Association. Vol. 49, No (Sep., 1954), pp
Kain, J; Quigley, J. Notes on Owner’s Estimates of Housing Values (in Applications). Journal of the American Statistical Association. Vol. 67, No (Dec., 1972), pp

4. Existing Literature, cont.
Research focused on sample groups with less than 600 observations.
Very large response errors are noted, but they were observed to be “offsetting.”(Kish, Lansing)
Net errors were small (less than 4%).
There are systematic differences based on socioeconomic characteristics.(Kain, Quigley)

5. Data Sources Block group shapefiles from the 1990 and 2000 US Census
Dallas County Appraisal District parcel data for 2000
NCTCOG city limit and roads shapefiles for Dallas County
House Price Indexes from the Office of Federal Housing Enterprise Oversight
GDP percentage growth records from the Bureau of Economic Analysis

6. Census Housing Values
Census Housing Values

7. Census Housing Values Collected on the long form questionnaire
Grouped by range value
Aggregated to the block group level
Freely and easily available
Based on likely subjective evaluations

8. Appraisal Housing Values
Produced by the Dallas County Appraisal District
Produced for the purposes of taxes
Uses “generally accepted appraisal techniques.” Dallas County Appraisal District ( 2007
Theoretically objective
Not widely available
Very large dataset

9. Analysis and Methodology
Appraisal parcel centroids were joined spatially to the Census block group files.
Census range observations were assigned the median value of their range.
The two datasets were compared.
The differences are discussed in terms of the economic circumstances, city variation, and growth variation.

10. Comparison

12. Results HUGE discrepancies are noticeable
What could cause these discrepancies in the data?
If we assume that the DCAD values are generally accurate, then census-takers are reflecting certain distortions or perceptions that seem to be mostly consistent across the entire county.
These discrepancies are discussed in the context of economic circumstances, city variation, and growth variations.

13. Optimism Effect? A result of economic optimism?
Either as a result of general economic increase (GDP growth) or improvements in the housing market?
2000 was a period of distinctly pleasant economic circumstances in both of the above contexts.

14. GDP Growth

15. US Housing Market US House Price Index 1990q1 1990q3 1991q1 1991q31 2 3 4 5 6 7 8
1990q1
1990q3
1991q1
1991q3
1992q1
1992q3
1993q1
1993q3
1994q1
1994q3
1995q1
1995q3
1996q1
1996q3
1997q1
1997q3
1998q1
1998q3
1999q1
1999q3
2000q1
2000q3
Year/Quarter
Percent Growth

16. Dallas Housing Market

17. City Variations?
Cities apply different laws, taxes, and regulations to each of its citizens.
Cities may also reflect different socioeconomic populations.
Is it possible for these variables to affect census reporting trends?

19. City Variations?

20. Growth Variation?
Logically, block groups seeing population growth are currently more desirable places to live. They should also see increasing house value if their desirability is increasing.
Is this reflected in census reports?
Does growth imbue citizens with greater optimism than DCAD appraisals reflect?

21. Variation by Growth?

23. Population Change Regression
SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION
Data set : PopRegress
Dependent Variable : CENSUSAPPR Number of Observations: 1682
Mean dependent var : Number of Variables : 2
S.D. dependent var : Degrees of Freedom : 1680
R-squared : F-statistic :
Adjusted R-squared : Prob(F-statistic) :
Sum squared residual: e+012 Log likelihood :
Sigma-square : e+009 Akaike info criterion :
S.E. of regression : Schwarz criterion :
Sigma-square ML : e+009
S.E of regression ML:
Variable Coefficient Std.Error t-Statistic Probability
CONSTANT
POP_CHAN_

26. Population Percentage Change Regression
SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION
Data set : PopRegress
Dependent Variable : CENSUSAPPR Number of Observations: 1682
Mean dependent var : Number of Variables : 2
S.D. dependent var : Degrees of Freedom : 1680
R-squared : F-statistic :
Adjusted R-squared : Prob(F-statistic) :
Sum squared residual: e+012 Log likelihood :
Sigma-square : e+009 Akaike info criterion :
S.E. of regression : Schwarz criterion :
Sigma-square ML : e+009
S.E of regression ML:
Variable Coefficient Std.Error t-Statistic Probability
CONSTANT
POP_PERC_

27. Conclusions
I must reject my hypothesis that the two datasets are similar. The discrepancy between the two datasets is very large.
Realistically they cannot be used for the same purposes.
Discrepancies seem highly correlated to more highly-valued properties.
Further studies using time series data to correlate economic status with reported values and to dig into the socio-economic variations that are observed in cities may be useful.

28. References
Agarwal, Sumit. The Impact of Homeowners' Housing Wealth Misestimation on Consumption and Saving Decisions. Real Estate Economics. Vol. 35, No. 2. (2007). pp. 135–154.
Bailar, Barbara. Some Sources of Error and their Effect on Census Statistics. Demography, Vol. 13, No. 2. (May, 1976), pp
Bancroft, G; Welch, E. Recent Experience with Problems of Labor Force Measurement. Journal of the American Statistical Association. Vol. 41, No (Sep., 1946), pp
Deming, W. On Errors in Surveys. American Sociological Review. Vol. 9, No. 4. (Aug., 1944), pp
Jaeger, Carol; Pennock, J. An Analysis of Consistency of Response in Household Surveys. Journal of the American Statistical Association. Vol. 56, No (Jun., 1961), pp
Kain, J; Quigley, J. Note on Owner’s Estimates of Housing Values (in Applications).
Journal of the American Statistical Association. Vol. 67, No (Dec., 1972), pp
Kiel, K; Zabel, J. The Accuracy of Owner-Provided House Values: The American Housing Survey. Real Estate Economics. Vol. 27, No. 2, (1999). 263–298.

29. References
Kish, L; Lansing, J. Response Errors in Estimating the Value of Homes. Journal of the American Statistical Association, Vol. 49, No (Sep., 1954), pp
Lansing, J; Eapen, A. Dealing with Missing Information in Surveys. Journal of Marketing. Vol. 24, No. 2. (Oct., 1959), pp
Marks, E; Mauldin, W. Response Errors in Census Research. Journal of the American Statistical Association. Vol. 45, No (Sep., 1950), pp
Neter, J; Wakesberg, J. A Study of Response Errors in Expenditures Data from Household Interviews. Journal of the American Statistical Association. Vol. 59, No (Mar., 1964), pp
O'Dell, Alan. An Assessment of the Errors in a Housing Survey. Urban Studies, Vol. 17, No. 2, (1980). pp. 217 – 222.
Robbins, P; West, R. Measurement Errors in the Estimation of Home Value (in Applications). Journal of the American Statistical Association. Vol. 72, No (Jun., 1977), pp
Wakesberg, J; Perkins, W. The Role of Evaluation in U.S. Censuses of Population and Housing. The Statistician. Vol. 20, No. 2, Population Censuses. (Jun., 1971), pp