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Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes

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  • Zachary H. Seeskin

Abstract

While commercial data sources offer promise to statistical agencies for use in production of official statistics, challenges can arise as the data are not collected for statistical purposes. This paper evaluates the use of 2008-2010 property tax data from CoreLogic, Inc. (CoreLogic), aggregated from county and township governments from around the country, to improve 2010 American Community Survey (ACS) estimates of property tax amounts for single-family homes. Particularly, the research evaluates the potential to use CoreLogic to reduce respondent burden, to study survey response error and to improve adjustments for survey nonresponse. The research found that the coverage of the CoreLogic data varies between counties as does the correspondence between ACS and CoreLogic property taxes. This geographic variation implies that different approaches toward using CoreLogic are needed in different areas of the country. Further, large differences between CoreLogic and ACS property taxes in certain counties seem to be due to conceptual differences between what is collected in the two data sources. The research examines three counties, Clark County, NV, Philadelphia County, PA and St. Louis County, MO, and compares how estimates would change with different approaches using the CoreLogic data. Mean county property tax estimates are highly sensitive to whether ACS or CoreLogic data are used to construct estimates. Using CoreLogic data in imputation modeling for nonresponse adjustment of ACS estimates modestly improves the predictive power of imputation models, although estimates of county property taxes and property taxes by mortgage status are not very sensitive to the imputation method.

Suggested Citation

  • Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:cpaper:2016-06
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    File URL: https://www.census.gov/content/dam/Census/library/working-papers/2016/adrm/carra-wp-2016-06.pdf
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    References listed on IDEAS

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    1. Benítez-Silva, Hugo & Eren, Selçuk & Heiland, Frank & Jiménez-Martín, Sergi, 2015. "How well do individuals predict the selling prices of their homes?," Journal of Housing Economics, Elsevier, vol. 29(C), pages 12-25.
    2. Pudney, Stephen, 2008. "Heaping and leaping: survey response behaviour and the dynamics of self-reported consumption expenditure," ISER Working Paper Series 2008-09, Institute for Social and Economic Research.
    3. Bruce Meyer & Robert Goerge, 2011. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Working Papers 11-14, Center for Economic Studies, U.S. Census Bureau.
    4. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    5. Bruce D. Meyer & Nikolas Mittag, 2015. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Upjohn Working Papers 15-242, W.E. Upjohn Institute for Employment Research.
    6. Doove, L.L. & Van Buuren, S. & Dusseldorp, E., 2014. "Recursive partitioning for missing data imputation in the presence of interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 92-104.
    7. Meyer, Bruce D. & Goerge, Robert M., 2011. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Contractor and Cooperator Reports 312394, United States Department of Agriculture, Economic Research Service.
    8. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    9. Charles F. Manski, 2015. "Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern," Journal of Economic Literature, American Economic Association, vol. 53(3), pages 631-653, September.
    10. Basu, Sabyasachi & Thibodeau, Thomas G, 1998. "Analysis of Spatial Autocorrelation in House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 61-85, July.
    11. Brittany Bond & J. David Brown & Adela Luque & Amy O’Hara, 2014. "The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey," CARRA Working Papers 2014-08, Center for Economic Studies, U.S. Census Bureau.
    12. repec:mpr:mprres:6479 is not listed on IDEAS
    13. John L. Czajka with other members of the Panel on the U.S. Census Bureau's Reengineered Survey of Income Program Participation. Constance F. Citro John Karl Scholz & Editors. Committee on National Sta, "undated". "Reengineering the Survey of Income and Program Participation," Mathematica Policy Research Reports 92ffe3e10fec4587859f12cb5, Mathematica Policy Research.
    14. Brady T. West & Roderick J. A. Little, 2013. "Non-response adjustment of survey estimates based on auxiliary variables subject to error," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(2), pages 213-231, March.
    15. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    16. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    17. Manski, Charles F., 2016. "Credible interval estimates for official statistics with survey nonresponse," Journal of Econometrics, Elsevier, vol. 191(2), pages 293-301.
    18. Quentin Brummet, 2014. "Comparison of Survey, Federal, and Commercial Address Data Quality," CARRA Working Papers 2014-06, Center for Economic Studies, U.S. Census Bureau.
    19. Manski, Charles F. & Molinari, Francesca, 2010. "Rounding Probabilistic Expectations in Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 219-231.
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