The paper develops a residential supply function for approved fixed-rate mortgages in US commercial banks as a first step to explain differences in origination patterns among groups of borrowers. It models the lender's decision to offer the borrower a risk-adjusted loan bundle relative to the terms on the credit application. The model includes multiple dependent variables that are risk-adjusted simultaneously to reflect accurately the loan officer's decision. Canonical correlation factor analysis is used to capture the lender's simultaneous decision. The loan-price ratio and contract interest rate are the most important variables in the lender supply function.
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References
1.
AngJ., and Will Hour, R. (1976) The Consumer loan supply function of a minority bank: an empirical note, Journal of Money, Credit, and Banking , 8, pp. 255-259.
2.
Bartlett, M.S. (1941-42) The statistical significance of canonical correlations , Biometrika, 22, pp. 29-37.
3.
Benston, G. and Horsky, D. (1979) Redlining and the demand for mortgages in the central city and suburbs, Journal of Bank Research, 10, pp. 72-87.
4.
Black, H. and Schweitzer, R. (1985) A canonical analysis of mortgage lending terms: testing for lending discrimination at a commercial bank, UrbanStudies, 22, pp. 13-19.
5.
Brueckner, J. (1993) Why do we have ARMs?, Real Estate Economics, 21, pp. 333-344.
6.
Canner, G., Gabriel, S. and Woolley, J.M. (1991) Race, default risk and mortgage lending: a study of the FHA and conventional loan markets, Southern Economics Journal, 58(1), pp. 249-262.
7.
Child, D. (1970) Essentials of Factor Analysis. New York: Holt, Rinehart, and Winston.
8.
Clauretie, T.M. (1987) The impact of interstate foreclosure cost differences and the value of mortgages in default rates, Journal of the American Real Estate and Urban Economics Association, 15, pp. 152-67.
9.
Clauretie, T.M. (1989) State foreclosure laws, risk shifting, and the private mortgage insurance industry, Journal of Risk and Insurance , 56, pp. 544-554.
10.
Cliff, N. and Krus, D. (1976) Interpretation of Canonical Analysis: Rotated vs. Unrotated Solutions, Psychometrika, 41, pp. 35-42.
11.
Elliehausen, G. and Lawrence, E. (1990) Discrimination in consumer lending, Review of Economics and Statistics, 72, pp. 156-160.
12.
Epley, D., Liano, K. and Haney, R. (1996) Borrower risk signaling using loan-to-value ratios , Journal of Real Estate Research, 11, pp. 71-86.
13.
Epley, D., Liano, K. and Haney, R. (1996) Impact of state foreclosure laws on the residential mortgage loan bundle approved by commercial banks. Paper presented to the Housing Finance Session, American Real Estate Society , Lake Tahoe.
14.
Follain, J. (1990) Mortgage choice, AREUEA Journal , 18, pp. 125-44.
15.
Galster, G. (1993) The facts of lending discrimination cannot be argued away by examining default rates, Housing Policy Debate, 4, pp.141-46.
16.
Hair, J., Anderson, R., Tatham, R. and Black, W. (1987) Multivariate Data Analysis, 2nd edn. New York: Macmillian Publishing Co.
17.
Haney, R.L. (1988) Sticky mortgage rates: some empirical evidence , Journal of Real Estate Research, 3, pp. 61-74.
18.
Hendershott, P. and Van Order, R. (1989) Integration of mortgage and capital markets and the accumulation of residential capital, Regional Science and Urban Economics, 19, pp. 189—210.
19.
Jameson, M., Shilling, J.D. and Sirmans C.F. (1990) Regional variations of mortgage yields and simultaneity bias, Journal of Financial Research, 12, pp. 211-218.
20.
Jud, G.D. and Epley, D. (1991) Regional differences in mortgage rates: an updated examination, Journal of Housing Economics, 1, pp.127-139.
21.
Kaiser, H. (1958) The varimax criterion for analytic rotation in factor analysis, Psychometrika, 23, pp. 187-200.
22.
Lambert, Z. and Durand R. (1975) Some precautions in using canonical analysis, Journal of Marketing Research, 12, pp. 468-75.
23.
Martin, R. and Smyth, D.J. (1991) Adverse selection and moral hazard effects in the mortgage market: an empirical analysis, Southern Economics Journal, 57, pp. 1071-1084.
24.
Meador, M. (1978) Interregional mortgage rate differentials, Federal Home Loan Bank Board Journal, pp. 2-6.
25.
Meador, M. (1982) Effects of mortgage laws on home mortgage rates , Journal of Economics and Business, pp. 143-148.
26.
Megbolugbe, I. and Cho, M. (1993) An empirical analysis of metropolitan housing and mortgage markets, Journal of Housing Research, 4, pp.191-224.
27.
Morrell, S.D. and Saba, R.P. (1983) The effects on FHLMC secondary market on regional mortgage yield differentials, Quarterly Review of Economics and Business, 23, pp. 85-98.
28.
Ostas, J.R. (1977) Regional differences in mortgage financing costs: a re-examination, Journal of Finance, 32, pp. 1774-1778.
29.
Perreault, W. and Spiro, R. (1978) An approach for improved interpretation of multivariate analysis, Decision Sciences, 9, pp. 402-413.
30.
Phillips-Patrick, F.J. and Rossi, C.V. (1996) Statistical Evidence of Mortgage Redlining? A Cautionary Tale, Journal of Real Estate Research, 11, pp. 13-24.
31.
Phillips, R. and Yezer, A. (1996) Self-selection and tests for bias and risk in mortgage lending: can you price the mortgage if you don't know the process?, Journal of Real Estate Research, 11, pp. 87-102.
32.
Sandor, R.L. and Sosin, H.B. (1975) The determinants of mortgage Risk Premiums: A Case Study of the Portfolio of a Savings and Loan Association, Journal of Business, 48, pp. 27—38.
33.
Schaff, A.H. (1966) Regional differences in mortgage financing costs , Journal of Finance, 21, pp. 85-94.
34.
Simonson, D., Stowe, J. and Watson, C. (1983) A canonical correlation analysis of commercial bank asset/liability structures, Journal of Financial and Quantitative Analysis, 18, pp. 125-140.
35.
Stowe, J., Watson, C. and Robertson, T. (1980) Relationships between the two sides of the balance sheet: a canonical analysis, Journal of Finance, 35, pp. 973-980.
36.
Zabrinski, S.J., Barth, J.R. and Marlow, M. (1982) Determinants of regional rates under varying economic conditions, Quarterly Review of Economics and Business, 22, pp. 81-100.
