Skip to main content
Log in

When Appraisers Go Low, Contracts Go Lower: The Impact of Expert Opinions on Transaction Prices

  • Published:
The Journal of Real Estate Finance and Economics Aims and scope Submit manuscript

Abstract

Using home purchase loan application data, we study buyer responses to the uncommon occurrence of the appraised value coming in below the contract price (i.e. a low appraisal), which sharply raises the probability of downward price renegotiation. We propose that two mechanisms drive the higher renegotiation rates. First, a liquidity channel, visible for financially constrained borrowers for whom a low appraisal impacts financing costs. Second, for financially unconstrained borrowers, we identify a news channel whereby the information content of the low appraisal alone induces borrower renegotiation. Importantly, we show that low appraisals result in lower renegotiated prices through these channels without a substantially lower likelihood of a loan application leading to loan origination or notably longer times from contract signing to sale.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Exceptions to requirement for appraisal are applications that receive a Property Inspection Waiver (PIW), available to about 5 % of Fannie Mae purchases after January 2017. Loans with PIWs do not appear in the analysis in this paper, as the focus here is on the relationship between the appraised value and the contract price.

  2. See Pagourtzi et al. (2003) or Vandell (1991) for an overview of the appraisal valuation methods and the comparable sales approach.

  3. Discussing the consequences of a low appraisal with realtors, we established that, in practice, there is virtually never legal resistance by sellers to a borrower withdrawing from the contract in the case of a low appraisal and obtaining a return of their earnest money deposit.

  4. We also remove cases where the appraisal does not occur at least one day after the data is initially entered into DU. This is in many cases just a matter of delayed entry of the DU underwriting data, however a key statistic in this paper is the effect of a low appraisal on time to close the loan from initial application date. Additionally, we want to be confident that the appraiser was selected by the same lender (potentially through an AMC) who closes the loan and that, for instance, a lender is not using an appraisal provided by the borrower from another pre-existing loan application.

  5. Appendix Figures 10a to 10f provide the same data by year. Effects are stable throughout this period.

  6. Analysis of the randomly selected 100 contracts was done manually. As such, this is not a method that is replicable for the entire sample of loan applications used in the analysis in order to specifically assess whether the contract is actually cancelled or just renegotiated.

  7. Recall that our sample only includes loan applications that were approved hence, once a lower FICO score borrower’s loan is in our sample, it is more likely to lead to a delivery.

  8. Analysis of mortgage-backed securities (MBS) issuance data from 2013 to 2018 reveals that 7.6% of Fannie Mae purchase mortgages in MBS issuance during this period had LTVs above 95%. This is markedly larger than the share with LTVs above 95% for Freddie Mac issuance, at 3.9%.

  9. Appendix Figure 11 shows analogous charts indicating the rate of upward renegotiation by LTV categories and we can observe that upward renegotiation rates are not related to LTV level in the same way that downward rates are.

  10. Appendix Figure 12 shows analogous charts displaying the percent of the difference between appraised value and contract price that is yielded by the buyer for appraised values that are above contract for different LTV categories. No evident relationship exists between LTV category and a willingness to yield or agree to a higher transaction price.

  11. Appendix Table 7 presents the regression results for the downward renegotiation probability for this subset of borrowers. The impact of relevant explanatory variables is similar to those for the main estimation sample.

  12. The 60% LTV is chosen because these borrowers have no additional Fannie Mae Loan-Level Pricing Adjustments for purchase mortgages. There is no additional charge for purchase borrowers with a FICO score of 660 or higher at this LTV value. 740, and up is obviously a subset of 660 and up and is chosen to obtain borrowers who are even less likely to face financing constraints.

  13. We tested various definitions of “unconstrained”, with little difference in estimated effects.

References

  • Abernethy, A., & Hollans, H. (2010). The home valuation code of conduct and its potential impacts. The Appraisal Journal, 78(1), 81–93.

    Google Scholar 

  • Agarwal, S., Ben-David, I., & Yao, V. (2015). Collateral valuation and borrower financial constraints: Evidence from the residential real estate market. Management Science, 61(9), 2220–2240.

    Article  Google Scholar 

  • Agarwal, S., Ambrose, B., & Yao, V. (2020). Can regulation de-bias appraisers?. Journal of Financial Intermediation, 44, 100827.

  • Ben-David, I. (2011). Financial constraints and inflated home prices during the real estate boom. American Economic Journal: Applied Economics, 3, 55–67.

    Google Scholar 

  • Bogin, A., & Nguyen-Hoang, P. (2014). Property left behind: An unintended consequence of a no child left behind “failing” school designation. Journal of Regional Science, 54(5), 788–805.

    Article  Google Scholar 

  • Calem, Paul S. and Lambie-Hanson, Lauren and Nakamura, Leonard I., Information Losses in Home Purchase Appraisals (March 2015). FRB of Philadelphia Working Paper No. 15-11, Available at SSRN: https://ssrn.com/abstract=2574689 or https://doi.org/10.2139/ssrn.2574689.

  • Calem, Paul S. and Lambie-Hanson, Lauren and Nakamura, Leonard I., Appraising Home Purchase Appraisals (2017-07-31). FRB of Philadelphia Working Paper No. 17-23, Available at SSRN: https://ssrn.com/abstract=3029756.

  • Chinloy, P., Cho, M., & Megbolugbe, I. (1997). Appraisals, Transaction Incentives, and Smoothing. Journal of Real Estate Finance and Economics, 14, 89–111.

    Article  Google Scholar 

  • Ding, L., & Nakamura, L. (2016). The impact of the home valuation code of conduct on appraisal and mortgage outcomes. Real Estate Economics, 44(3), 658–690.

    Article  Google Scholar 

  • Duca, J., Muellbauer, J., & Murphy, A. (2010). Housing Markets and the Financial Crisis of 2007–2009: Lessons for the Future. Journal of Financial Stability, 6, 203–217.

    Article  Google Scholar 

  • Eriksen, M., Fout, H., Palim, M., & Rosenblatt, E. (2019). The influence of contract prices and relationships on appraisal Bias. Journal of Urban Economics, 111, 132–143.

    Article  Google Scholar 

  • Eriksen, M., Fout, H., Palim, M., & Rosenblatt, E. (2020). Contract Price confirmation Bias: Evidence from repeat appraisals. Journal of Real Estate Finance and Economics, 60(1), 77–98.

    Article  Google Scholar 

  • Figlio, D., & Lucas, M. (2004). What’s in a grade? School report cards and the housing market. American Economic Review, 94(3), 591–604.

    Article  Google Scholar 

  • Fout, H., & Yao, V. (2016). Housing market effects of appraising below contract. Working paper, available at: https://www.researchgate.net/profile/Vincent_Yao/publication/298807852_Housing_Market_Effects_of_Appraising_Below_Contract/links/57c04c7508ae2f5eb3321d07/Housing-Market-Effects-of-Appraising-Below-Contract.pdf.

  • Genesove, D., & Mayer, C. (1997). Equity and time to Sale in the real estate market. American Economic Review, 87(3), 255–269.

    Google Scholar 

  • Genesove, D., & Mayer, C. (2001). Loss aversion and seller behavior: Evidence from the housing market. Quarterly Journal of Economics, 116, 1233–1260.

    Article  Google Scholar 

  • Goodman, J., & Ittner, J. (1992). The accuracy of homeowners' estimates of house value. Journal of Housing Economics, 2(4), 339–357.

    Article  Google Scholar 

  • Han, L., & Strange, W. (2016). What is the role of the asking price for a house? Journal of Urban Economics, 93, 115–130.

    Article  Google Scholar 

  • Hendershott, P., Hendershott, R., & Shilling, J. (2010). The mortgage finance bubble: Causes and corrections. Journal of Housing Research, 19(1), 1–16.

    Article  Google Scholar 

  • Horne, David K. and Rosenblatt, Eric, Property Appraisals and Moral Hazard (June 1996). Available at SSRN: https://ssrn.com/abstract=9124.

  • Kelly, A. (2006). Appraisals, automated valuation models, and mortgage default. Federal Housing Finance Agency: Working Paper.

    Book  Google Scholar 

  • Kiel, K., & Zabel, J. (1999). The accuracy of owner-provided house values: The 1978–1991 American housing survey. Real Estate Economics, 27(2), 263–298.

    Article  Google Scholar 

  • LaCour-Little, M., & Green, R. (1998). Are minorities or minority neighborhoods more likely to get low appraisals? Journal of Real Estate Finance and Economics, 16(3), 301–315.

    Article  Google Scholar 

  • Levitt, S., & Syverson, C. (2008). Market distortions when agents are better informed: The value of information in real estate transactions. Review of Economics and Statistics, 90(4), 599–611.

    Article  Google Scholar 

  • Merlo, A., & Ortalo-Magné, F. (2004). Bargaining over residential real estate: Evidence from England. Journal of Urban Economics, 56(2), 192–216.

    Article  Google Scholar 

  • Mian, A., & Sufi, A. (2010). Household leverage and the recession of 2007 to 2009. IMF Economic Review, 58(1), 74–117.

    Article  Google Scholar 

  • Murray, J. K. (2009). Issues in appraisal regulation: The cracks in the foundation of the mortgage lending process. Loy. LAL Rev., 43, 1301. Available at: https://digitalcommons.lmu.edu/llr/vol43/iss4/4.

  • Nakamura, L. (2010). How much is that home really worth? Appraisal Bias and Home Price Uncertainty. Business Review, Federal Reserve Bank of Philadelphia, Q1, 11–22.

    Google Scholar 

  • Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T., & French, N. (2003). Real estate appraisal: A review of valuation methods. Journal of Property Investment & Finance, 21(4), 383–401.

    Article  Google Scholar 

  • Rutherford, R., Springer, T., & Yavas, A. (2005). Conflicts between principals and agents: Evidence from residential brokerage. Journal of Financial Economics, 76(3), 627–665.

    Article  Google Scholar 

  • Shi, L., & Zhang, Y. (2015). Appraisal inflation: Evidence from the 2009 GSE HVCC intervention. Journal of Housing Economics, 27, 71–90.

    Article  Google Scholar 

  • Shui, J., & Murthy, S. (2019). Under what circumstances do first-time homebuyers overpay? — An empirical analysis using mortgage and appraisal data. Journal of Real Estate Research, 41(1), 107–145.

    Article  Google Scholar 

  • Stigler, G. (1961). The economics of information. Journal of Political Economy, 69(3), 213–225.

    Article  Google Scholar 

  • Taylor, C. (1999). Time-on-the-market as a sign of quality. Review of Economic Studies, 66(3), 555–578.

    Article  Google Scholar 

  • Vandell, K. D. (1991). Optimal comparable selection and weighting in real property valuation. AREUEA Journal, 19(2), 213–239.

    Article  Google Scholar 

  • Zhu, S., & Kelley Pace, R. (2012). Distressed properties: Valuation Bias and accuracy. Journal of Real Estate Finance and Economics, 44, 153–166.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the journal editor and anonymous referees for their reviews. We thank Sumit Agarwal, Mike Eriksen, and Vincent Yao for feedback on earlier versions of the paper. We also thank Franklin Carroll, Steven Corbin, Casey Jones, and Foong-Yin Wong for research assistance and the experts in Fannie Mae’s Loan Quality Center who read and interpreted home purchase contracts.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nuno Mota.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Fig. 11
figure 11

Upward renegotiation rates by LTV category and appraised value minus contract. * “Unconstrained” borrowers defined as those with a post-appraisal LTV below 60% and FICO of 740 or highe

Fig. 12
figure 12

Median percent of difference between appraised value and contract yielded by buyers in cases where upward renegotiation occurs. * “Unconstrained” borrowers defined as those with a post-appraisal LTV below 60% and FICO of 740 or higher

Table 7 Modeling downward renegotiation for the unconstrained borrower group1

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fout, H., Mota, N. & Rosenblatt, E. When Appraisers Go Low, Contracts Go Lower: The Impact of Expert Opinions on Transaction Prices. J Real Estate Finan Econ 65, 451–491 (2022). https://doi.org/10.1007/s11146-020-09800-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11146-020-09800-6

Keywords

Navigation