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The Demand for Home Equity Loans at Bank X*

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The Demand for Home Equity Loans at Bank X* An MBA 555 Project Laura Brown Richard Brown Jason Vanderploeg *bank name withheld for proprietary reasons. Introduction - PowerPoint PPT Presentation

Text of The Demand for Home Equity Loans at Bank X*

  • The Demand for Home Equity Loans at Bank X* An MBA 555 Project

    Laura BrownRichard BrownJason Vanderploeg

    *bank name withheld for proprietary reasons

  • Introduction

    The current market for home equity loans is highly competitive. Due to the massive housing slowdown, demand for equity transactions has also slowed, forcing companies to re-strategize in a changing environment. We have endeavored to develop a model to better equip Bank X decision makers as they pursue strategies for capturing a larger share of the market within the banks national footprint.

  • Project ObjectiveConstruct a demand modelfor variables affecting

    the volume of equity loans (demand variable Q), focusing especially on the effect of

    the bank prime loan rate (demand variable P)

  • Hypotheses Tested

    H1 = The demand for home equity loans is explained by interest rates offered by banks (prime rate)

    H2 = The demand for home equity loans is explained by consumer purchasing power.

    H3 = The demand for home equity loans is explained by public consumer economic indicators (stock market)

    H4 = The demand for home equity loans is explained by advertising expenses.

  • Overview of Methodology

    Stage 1

    Collected monthly data sets (2003 to August 2006)Created independent & dummy variables to test pattern behaviorUsed stepwise regression and practical considerations to eliminate variables

    Stage 2

    Used OLS to test the 4 basic assumptions of regression analysisGenerated regression charts

    Stage 3

    Generated estimation modelIdentified and interpreted elasticitiesSummarized final results

  • Stage 1

    Variables Examined

    Volume of Home Equity loansBank Loan Prime RateFederal Funds Rate# of Houses SoldMedian Price of Houses SoldConsumer Loans @ Commercial BanksTotal # of Loan UnitsFirm AdvertisingResidential Energy ConsumptionTransportation Energy ConsumptionMoney Supply (Stocks)

  • Stage 1

    Definition of Remaining Variables

    VariableTypeHypothesized Sign

    Demand for Home Equity LoansDependentBank Prime Loan Rate (Proxy)ExogenousNegativeMoney Supply (Stocks)ExogenousNegativeTotal UnitsEndogenousPositiveAdvertisingEndogenousPositiveFed Funds RateExogenousNegativeConsumer Price IndexExogenousPositiveConsumer Loans @ Comml BanksExogenousNegativeMedian Price of Houses SoldExogenousPositive

  • Stage 2

    Assumption of Non-Collinearity

    Multicollinearity, as measured by VIF, takes place when an independent variable correlates with other independent variables. VIF under 10 is preferred, and under 5 is ideal.

    Parameter VIFs:Bank Loan Prime Rate 2.290Money Supply (Stocks) 2.091Total Units 1.142

    Average VIF for model 1.841

  • Stage 2

    Assumption of Absence of Autocorrelation

    OLS requires that the residual error terms show no discernible pattern. The assumption is violated when the Durbin-Watson test shows either + or - autocorrelation.

    The model revealed evidence of auto correlation.Rho: Pos & Neg RejectRho: Positive Do Not RejectRho: Negative Reject

    Using First Differences to remove the autocorrelation did not improve the model.

  • Stage 2

    Assumption of Constant Variance

    Constant variance means that all random error terms have the same variance and are not correlated to one another. The null hypothesis of Whites test assumes this homeskedasticity is in place. At 95% confidence, a p-value of 0.05 or higher allows us to accept the null hypothesis.

    The p-value for Whites in our model was 0.094

  • Stage 2

    Assumption of Normality

    Normality describes the fact that remaining random error terms exhibit a normal distribution. The chart for residual error terms should produce a line angled at approximately 45 degrees.

    The models correlation for normality was .992, well above the critical value of .977.

  • Stage 2

    Predictive Ability Chart

  • Stage 2

    Confidence Intervals Chart

  • Stage 2

    Constant Variance Chart

  • Stage 2

    Normal Probability Chart

  • Stage 2

    Error Bars Chart

  • Stage 3Estimated Model and Results

    All data sets were entered into WinORS. After a stepwise regression, Ordinary Least Squares and logarithmic transformation, the following model was constructed for quantity of home equity loans:

    lnQHE = f(-.0268Ln(P) .2188Ln(M) + .977Ln(U))

    The F-statistic which measure the explanatory power of the model was found to be significant at 371.1.

    The p-value was 0.00001, showing that the model is statistically significant at a 99.9% confidence level.

  • Stage 3

    Estimated Model and Results, cont.

    The coefficient of determination R2 measures the degree of variation in the dependent variable that can be explained by variation in the independent variables.

    Our model showed excellent scores:

    R2 = 96.532%Adjusted R2 = 96.272%

  • Stage 3

    Elasticities

    Elasticities measure the % change in the dependent variable, given a 1% change in the independent variable.

    In the multiplicative demand model elasticities are revealed to be constant at all points on the demand curve.

    Parameter estimates represent the elasticities of the independent variables.

    Absolute values < 1 are inelastic, values >1 are elastic.

  • Stage 3Elasticities, cont.

    The model reveals that home equity loans are price inelastic:

    Parameter estimate for bank loan prime rate = -0.268A 10% increase in the prime rate would result in just 2.7% decrease in the demand for home equity loans. This parameter is inelastic.

    The model also reveals that home equity loans are elasticrelative to the availability of other funding for consumerspending.

    Parameter estimate for money supply = -2.188 A 10% increase in the availability of alternative funding would result in a 21.9% decrease in the demand for home equity loans.

  • Stage 3 Conclusions

    Demand is not heavily affected by interest rate change, so the bank can take advantage of the inelastic relationship and achieve higher revenues.H1 is accepted

    Alternative funding sources for consumer spending has an inverse relationship to the demand of home equity loans. In a bullish market, consumers dont borrow against their equity.H3 is accepted

    Consumer purchasing power and firm advertising has little statistical significance in the demand for home equity loans.H2 and H3 are rejected

  • Appendix AExcerpts from Industry Literature

    Home-Equity Borrowing Stalls As the Housing Market CoolsRuth Simon

    The slowdown in home-equity borrowing is leading to weaker sales in some markets for autos, building materials and electronics

    As rates go up there is unknown future demand for home equity loans

    During the housing boom, demand for home-equity lines of credit climbed sharply as property values rose

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