Using the Housing Affordability Index to Forecast Home Values

In recent years, the Real Estate industry has seen prices rebound and, due to the economic recovery, unemployment rates have been drastically reduced from the peak of the recession.

However, with uncertainty still swirling around interest rate expectations, and with stagnant household income growth over the past decade, there is a fundamental question facing Credit Unions: what does the future of the housing market look like?

The Housing Affordability Index (HAI) – A model that can help

To help forecast the housing market, it is important for Credit Unions to evaluate the impact that changing economic conditions will have on their portfolio’s performance. One tool that can help model changing economic conditions is the Housing Affordability Index.

Promoted by the National Association of Realtors (NAR), the HAI models how home prices, interest rates, and household income interact and affect the ability of an average household to obtain a home mortgage. This is quantified by computing the financial ability of an average family to qualify for a mortgage loan on a median-priced home using the macroeconomic data of median home price and median household income.

A Housing Affordability Index score of 100 is representative of an average household that earns just enough income to qualify for a loan on the average home. Any value above 100 indicates that the average household earns more than enough income to qualify, and an index value below 100 indicates that the average household does not earn enough income to qualify.

For some context, in 2005, near the peak of the last major housing boom, the HAI was 113.2 according to the NAR. The most recent measure released by the NAR, in April 2016, calculated the index to be 162.4. The large difference between these two figures simply demonstrates that housing is more affordable for the average family now than what it was in 2005 when prices peaked.

Calculating the Housing Affordability Index

By using the Housing Affordability Index, we can forecast if buying a home is, or will be, affordable for the average family, and judge if home values are expected to increase or decrease under different scenarios.

This takes us back to our original question: what does the future of the housing market look like? As mentioned earlier, the industry expectation is that interest rates are going to go up, although it is not universally known when or by how much. Because middle class Americans are the life-blood of the housing market, and their behavior is sensitive to changes in interest rates, it is important to forecast how interest rate changes will affect the market.

If we take an incremental approach to this, and separately assume a 100 bps, 200 bps, and 300 bps increase in interest rates, we can see how the increases in interest rates limit the purchasing power of the average family. To provide an example, the following is National data available as of April 2016:

Using a simple “=PMT()” function in Excel, we  can calculate that in the given scenario, the average monthly payment (assuming 80% originating loan-to-value) is $881, which comes out to about 15.4% of an average household’s income. Based on this, we can calculate the level of income that the average household would have to earn to receive a loan.  The NAR models annual housing costs at 25% of income. Thus, by annualizing the monthly payment of $881 and dividing it by 0.25, qualifying income is calculated as roughly $42,300. The resulting HAI is approximately 162.4 (calculated as 68,700/42,300 x 100). Below we can observe the same calculations as of April 2016 broken out regionally across the United States:

Using the Housing Affordability Index to Model Real Estate Values

On its own, the HAI is informative, but where it really adds value, is in scenario analysis. Performing the same computations shown above, in a scenario where interest rates increase by 300 bps, the monthly payment for a $233,700 home is calculated at $1,230, or 21.5% of income. As a result, the average home value would be expected to decrease because household purchasing power has been eroded by the increase in cost to borrow funds The HAI in this scenario would be 116.4 (calculated as 68,700/ (1,230 x 4 x 12)) suggesting the increase in interest rates may cause median home prices to fall as low as $167,300 (116.4/162.4 x 233,700).

To summarize, a family could purchase a median-level priced home ($233,700) in the current interest rate environment with a monthly payment of $881. However, that $881 payment would only be enough to purchase a $167,300 home if interest rates were increased by 300 bps, a decrease of $66,400 or 28.4%.

As mentioned above, by taking an incremental approach to this, we could work through the same calculations in other interest rate environments. The table below shows the results when considering different interest rate environments:

Other Considerations

Certainly, the sensitivity analysis on interest rates and the decrease in purchasing power illustrated in the above table does not occur in a vacuum. Market forces will react once interest rates go up, and the factors of supply and demand are dynamic across different rate levels. For instance, if rates increase as modeled above, it is likely that demand for mortgages will go down, particularly in the refinance market, thus dragging down prices and increasing purchasing power.

On the other hand, the increased rates may also encourage people considering a move to stay put in their current fixed rate mortgages, limiting the supply of properties, and pushing prices higher. Such market forces would somewhat offset each other, but would also affect the numbers reported in the chart above if built into the model.

Call to Action

The example above illustrates why it is important for Credit Unions to understand how economic fluctuations affect home values. Twenty Twenty Analytics factors these subsequent affects on home prices into our portfolio analysis model by stress testing real estate values in varying scenarios of market fluctuations, resulting in a model that predicts Net Worth ramifications and Allowance for Loan Loss consequences based on these macroeconomic changes.

By understanding how interest rates and household incomes affect home values, and how home values affect the balance sheet, a Credit Union can more efficiently allocate assets, identify profitable products, and design marketing campaigns.

For more information on how Twenty Twenty Analytics can help with modelling and stress testing your portfolio, feel free to reach out to Alan Veitengruber or Dan Price and we would be glad to discuss.

-Alan Veitengruber
Twenty Twenty Blogger

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