4. Empirical Result
4.2 Reaction on information quality
In this section we will investigate adjustment influence factors. Firstly, we test the rational behavior of appraisers reacting to low quality information. We replace the
“noise” proxy variable with the difference rate of market-extracted values. As the value information extracted from the market has greater variation, the appraiser will take insufficient comparatives and know less about the market, or need to place far more adjustment magnitude on property characteristics. Secondly, the type of reference point may have a different impact on the appraiser's level of conservatism; appraisers could have more confidence in their own appraised value rather than in others' valuations.
Finally we investigate whether the client background will affect the adjustment pattern, a hypothesis that the size of clients will affect the adjustment parameters will be tested.
From equation (4), we rewrite Quan-Quigley model to be equation (9). That is, appraisers will partial adjust to the market change, the difference of contemporaneous market information and last appraised value.
[
, *, 1]
The parameter K is what we concern the weight of appraiser put on market information.
To avoid K parameter to be zero and not to set aside the unchanged value, we define the dependent variable to be level of conservatism or named anchoring degree (AD), 1-K.
The adjustment influence factors model is specified as follow:
ε β
α ⋅ + +
=
∑
= l
n
l
lD
noise AD
1 ……….…………..（10）
Noise is defined as the absolute value of the ratio of the difference between comparison
value and capitalization value to the comparison value, comps
cap comps
P P noise P −
=
. Higher difference between comparison value and the capitalization value means more noise in the market. A dummy variable set is to test whether reference point and client size affects the adjustment. The variable description is in Table 5.
Table 6 shows that the regression model is significant at 1% level. T-REITs appraisers do react conservatively to low market information as noise increases. The result is the same with Clayton et al. (2001). The dummy set of reference point types shows appraisers refer to transaction prices but not other appraiser’s opinion. Appraisers have less anchoring effect to transaction prices, which means that appraisers have more confidence in their own judgment. Moreover, the model result shows the larger the client is, the more conservative the adjustment strategy.
Table 5. Variable description
Variable Mean Std. Dev. measurement Description
AD* 0.7786 0.4465 continuous How conservative appraisers are when reappraised trust property
noise 0.0243 0.0219 continuous Proxy variable of market comparison quality
D1 0.0426 discrete Categories of reference point (other appraisers’ opinion=1, other=0)
D2 0.0213 discrete Categories of reference point (property transaction price =1, other=0)
D3 0.5957 discrete Relative size of clients (financial holding co.
as originator =1, others=0)
* Notes as dependent variable.
Table 6. Results of =α ⋅ +
∑
β +ε= i n
i
iD
noise AD
1
Variable. Coefficient Std. Err. T-value
noise 8.772 *** 2.680 3.273
D1 -.029 0.339 -0.084
D2 -1.064 ** 0.470 -2.262
D3 .539 *** 0.118 4.572
R-squared = 0.702 Adjusted R-squared = 0.493 F（4,90）= 21.883 Prob.= 0.00000***
***Significant at 1% level.
** Significant at 5% level.
4. Conclusion
Regression results show that we reject the jointly null hypothesis of full adjustment to market fluctuations and the confidence parameter is 0.84. We find that appraisers have partial adjustment strategies. Moreover, we find appraisers give less weight to current market information because of market noise. Noise does decrease appraisers’
confidence. That means appraiser’s partial adjustment is a rational behavior in T-REIT’s reappraisal. The result is similar to Quan and Quigley (1991).
Reference
1. Akerlof, G. A. and R. J. Shiller , 2009, Animal Spirits—How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism, Princeton University Press.
2. Brown, G. R., and G. A. Matysiak, 1998, “Valuation Smoothing without Temporal Aggregation”, Journal of Property Research, 15(2):89-103.
3. Clayton, J., D. Geltner, and S. W. Hamilton, 2001, “Smoothing in Commercial
Property Valuation: Evidence from Individual Appraisals”, Real Estate Economics, 29(3) 337-360.
4. Diaz, J., 1997, “An Investigation into the Impact of Previous Expert Value Estimate on Appraisal Judgment”, Journal of Real Estate Research, 13(1):57-66.
5. Diaz, J. and J. A. Hansz, 1997, “How Valuers Use the Value Opinions of Others”, Journal of Property Valuation and Investment, 15(3):256-260.
6. Diaz, J. and M. Wolverton, 1998, “A Longitudinal Examination of the Appraisal Smoothing Hypothesis”, Real Estate Economics, 26(2):349-358.
7. Fisher, J., M. Miles, and R. Webb, 1999, “How Reliable are Commercial Property Appraisals? Another Look”, Real Estate Finance, 16(3):9-15.
8. Geltner, D., 1989a, “Bias in Appraisal-Based Returns”, Journal of the American Real Estate and Urban Economics Association (AREUEA Journal), 17(3): 338–352.
9. Geltner, D., 1989b, “Estimating Real Estate’s Systematic Risk from Aggregate Level Appraisal-Based Returns”, Journal of the American Real Estate and Urban Economics Association, 17(4): 463–81.
10. Geltner, D., 1991, “Smoothing in Appraisal-Based Returns”, Journal of Real Estate Finance and Economics, 4:327-345.
11. Geltner, D., 1998, “Appraisal Smoothing: The Other Side of the Story—A Comment”, Available at SSRN: http://ssrn.com/abstract=131459, Working Paper, Department of Finance, University of Cincinnati.
12. Hansz, J. A., 2004, “Prior Transaction Price Induced Smoothing: Testing and Calibrating the Quan-Quigley Model at the Disaggregate Level”, Journal of Property Research, 21(4):321-336.
13. Ibbotson, R., and L. B. Siegel, 1984, “Real Estate Return: a Comparison with Other Investment”, AREUEA, 12:219-242.
14. Kahneman, D. and A. Tversky, 1973, “On the Psychology of Prediction”, Psychological Review, 80(4):237-251.
15. Lai, T.Y., and K. Wang, 1998, “Appraisal Smoothing: The Other Side of Story”, Real Estate Economics, 26(3): 511-535.
16. Mcallister, P., A. Baum, N. Crosby, P. Gallimore, and A. Gray, 2003, “Appraiser
behaviour and appraisal smoothing: some qualitative and quantitative evidence”, Journal of Property Research, 20(3):261-280.
17. Quan, D. C., and J. M. Quigley, 1991, “Price Formation and the Appraisal Function in Real Estate Markets”, Journal of Real Estate Finance and Economics, 4: 127-146.
18. Ross, S., and R. Zisler, 1987, “Managing Real Estate Portfolio, Part 3: A Close look at Equity Real Estate Risk”, Goldman Sachs Real Estate Research, November.
19. Yiu, C. Y., B. S. Tang, Y. H. Chiang, and L. H. Choy, 2006, “Alternative Theories of Appraisal Bias”, Journal of Real Estate Literature, 14(3):321-344.
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