Sample Report Market Sensitivity 30 Year, Fixed, Conforming Mortgages

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1 Sample Report Market Sensitivity 30 Year, Fixed, Conforming Mortgages DATA is for informational purposes and is not specific to any bank 2010 Heitman Analytics 1

2 Executive Summary Determine the relationship between market rates and a specific product performance. Period: 7/01/ /30/09 Heitman Market Rate compared against Bank Rate Heitman Analytics Conclusions: I. A 10 basis point spread from the market rate has caused volume to respond by $39,500,000. II. During 2009, 4 th quarter market rates have become more highly correlated. III. Increased volume response is observed from overall market stabilization Heitman Analytics 2

3 Abstract The purpose of this report is to provide a quantitative understanding of product sensitivity to aggregate market movements. The duration of this study analyzes data collected from July 1 st, 2009 to November 30 th The Data This report is built on a foundation of time series data. The primary variables used in this report are: market rate, the bank s lending rate, and the bank s volume for 30 year fixed conforming loans. The market rate is generated in house at Heitman Analytics. This number represents a National weekly average rate for 30 year, fixed conforming mortgages. The quoted bank lending rate is a weekly average rate specific to 30 year fixed conforming mortgages. Bank volume is the sum of total mortgage volume locked for a given week. (Market volume is not disclosed in this report, but can be included upon request.) 5.4% $900,000, % $800,000, % $700,000, % $600,000, % $500,000, % $400,000, % $300,000, % Jul Jul Aug Aug Aug Sep Sep Oct Oct Nov Nov $200,000,000 Market Rate Bank Rate Bank Volume 2010 Heitman Analytics 3

4 Heitman Analysis A multi variable regression was used to generate the desired elasticity relationships. When specifying the economic model, the Log Log or constantly elasticity model was chosen. After rigorous tests, this model has proved to be the most robust and statistically significant. Heitman Analytics generates an in house representation of the National rate for mortgages as well as specific regional rates. A national rate was chosen for use in this report. A spread was then generated depicting the rate difference between the specific bank rate, and the market. (see graph right) The spread was regressed and compared against the specific bank rate and volume. $4,000,000,000 $3,500,000,000 $3,000,000,000 Bank volume was forecasted using only rate differential. (see graph left) $2,500,000,000 $2,000,000,000 $1,500,000,000 $1,000,000,000 $500,000,000 $0 Mar Mar Apr May Jun Jul Aug Sep Oct Volume Indicated by Rate Diffential from Market Actual Volume Conclusion A 10 basis point spread from the market rate has caused volume to respond by $39,500, Heitman Analytics 4

5 Appendix I. Charts Isolated view of Market Rate and Bank Rate Rate forecast compared against actual Bank rate Value Used Bank rate = Market Rate(.762) % 2010 Heitman Analytics 5

6 II. Regression outputs used in findings 2010 Heitman Analytics 6

7 III. Forecast models used in report 5,000,000,000 4,000,000,000 3,000,000,000 2,000,000,000 1,000,000, ,000,000,000 Forecast: VOLUME_THIF Actual: VOLUME_THIRTYFIX Forecast sample: 3/02/ /12/2009 Included observations: 33 Root Mean Squared Error 5.47E+08 Mean Absolute Error 4.46E+08 Mean Abs. Percent Error Theil Inequality Coefficient Bias Proportion Variance Proportion Covariance Proportion ,000,000, m4 2009m5 2009m6 2009m7 2009m8 2009m9 VOLUME_THIF ± 2 S.E. 5,000,000,000 4,000,000,000 3,000,000,000 2,000,000,000 1,000,000, ,000,000,000 Forecast: VOLUME_THIF Actual: VOLUME_THIRTYFIX Forecast sample: 3/02/ /12/2009 Included observations: 33 Root Mean Squared Error 8.40E+08 Mean Absolute Error 6.81E+08 Mean Abs. Percent Error Theil Inequality Coefficient Bias Proportion Variance Proportion Covariance Proportion ,000,000, m4 2009m5 2009m6 2009m7 2009m8 2009m9 VOLUME_THIF ± 2 S.E Heitman Analytics 7

8 Glossary a. Adjusted R(squared) Similar to R(squared), the term is adjusted in a way that penalizes you for adding excessive amounts of explanatory variables. b. Akaike info criterion A common measure of goodness of fit for a given economic model. c. Autocorrelation A statistical data property, when in regression the residual from a given data point is highly correlated with the residual from the next data point. d. Bi variant Regression A regression with only two variables specified. e. F Statistic Similar to the Test statistic, F static is used to determine the relevance of the entire specified model, not a single coefficient. f. Hannan Quinn Criterion Used as a guide to determine which economic model to use. g. Heteroskedasticity Used to indicate unequal distribution at given data points. h. Lagged Price Price that is used from previous day to indicate response time. i. S.E. of Regression Standard Error of Regression j. Schwarz Criterion Evaluation tool used to determine which economic model to use when constructing a regression. The statistic penalizes you for complexities, encouraging for a simpler model to increase robustness. k. Test Statistic A summary statistic for a given data set, used in hypothesis testing to determine relevance and probability of a given coefficient. l. Regression The process of constructed a best fit line through a series of data points, using ordinary least squares. m. R (squared) A statistical measure to evaluate the goodness of fit for a line drawn through data points. Shown as a value between 0 and 1, with 0 being no fit line possible, and 1 being a perfectly fit line Heitman Analytics 8