California Foreclosures Vs. Housing Costs

California Foreclosures vs. Housing Costs

Regression Analysis is defined as another technique for measuring the linear association between x (independent variable) and y (dependent variable) and shown as (Y= a +b1X1 +b2X2+b3X3...+bnXn) which is used extensively in forecasting. For Team B’s research, we are going to run a regression analysis on foreclosures versus housing costs in the state of California. In regression, the independent variables are hypothesized to affect the dependent variable in an additive and linear way. An F-test can be used in regression, which is a procedure to determine whether there is more variability explained by the regression or unexplained by the regression. The real question is “are skyrocketing housing costs the reason for so many new foreclosures?” The number of foreclosure notices sent to California homeowners last quarter increased to its highest level in almost ten years, is this the result of flat appreciation, slow sales, and post teaser-rate mortgage resets? Or is that folks are just getting in way over their heads?

Defaults tend to happen after a certain length of time and today's activity reflects a peak in the number of home loans made back in the summer of 2005. Additionally, the loans being made back then were riskier because of the subprime activity, as well as higher appreciation rates. It's easier to make a loan when the security for that loan is going up in value, than when values are flat," said Marshall Prentice, DataQuick's president. Almost of the loans that went into default last quarter were originated between April 2005-May 2006. The median age was 15 months. Loan approvals peaked in August 2005. Adjustable-rate mortgage use for primary purchase home loans peaked at 77.8% in May 2005 and has since been on the decrease. On primary mortgages, homeowners were an average of five months behind on their payments when the lender started the default process. The homeowners owed a median of $10,784 on a median $331,200 mortgage.

Although the state as a whole has a significance increase in foreclosures, the default numbers reflect wide regional differences. The first-quarter foreclosures were a record in Riverside, Sacramento and Contra Costa counties. In Los Angeles County it was almost 60 percent below the first-quarter 1996 peak, reflecting the depth of the recession in the mid-1990s. On a loan-by-loan basis, mortgages were least likely to go into default in Marin, San Francisco and San Mateo counties. The likelihood was highest in Sacramento, Riverside and San Joaquin counties.

The formula used in linear regression analysis is the same formula used in algebra, known as the “linear