“In the spring a young man’s fancy lightly turns to thoughts of buying a house,” Morty said, as he put the latest issue of the Financial Analysts Journal on my desk.
We don’t often paraphrase Tennyson in the office, so this caught my attention. “You’re not thinking of moving again, are you?” I said.
“No, but I always keep my eye on the market,” he said. “You should check out what these economists are saying.”
So I did.
First I read the FHFA working paper, by the team of Alexander Bogin, William Doerner, and William Larson. “Missing the Mark: Mortgage Valuation Accuracy and Credit Modeling” appears in the Financial Analysts Journal Q1 2019, p. 32.
Then, Will Larson, senior economist at the Federal Housing Finance Agency, accepted our request for an interview.
Q: In general, what draws you to research the area of housing prices?
A: Housing is just one part of a giant web of interrelated factors within a city that includes firms, recreation, innovation, and transportation systems. Because all these things are related, house prices are one of the primary ways of measuring how much people value certain locations over others, the state of a city’s economy, and the desirability of an area—whether it’s to have fun or raise a family. There’s also the financial angle. When people borrow money to buy a house, the house is typically the collateral on the loan. If we can measure how the value of the house changes, it will tell us something about whether the borrower may default on that mortgage. Basically, house prices are such a fundamental unit of measurement to a whole host of applications that it’s easy to be drawn to the subject.
Q: Your paper states, “Price indexes… could help investment managers monitor assets that are heterogeneous or infrequently traded.” What is the biggest barrier to creating a localized house price index?
A: The biggest barrier to creating a localized house price index is that homes are not transacted frequently, and they are all different in their own ways. This is a fundamental problem that shows up in a lot of investment vehicles, including art, wine, trading cards, and other collectibles. To measure a “price index” that strips away the differences between individual items requires the use of statistical techniques. To do this for housing, we need multiple home transactions within a given area within a certain time period. If the area is too small, we simply don’t have enough sales for the index to be statistically valid.
Q: Will big-data analytics affect the drive toward real estate price indexes?
A: Big-data analytics will help house price measurement to some extent, but it is critical to incorporate urban economics concepts to get the full picture. Big-data methods typically are agnostic about relationships between entities within a dataset. Urban economics lets us guide the data and the analysis in ways that are beneficial to getting a good measurement.
Q: Your article states, “Perhaps counter to some expectations, the most granular index is not always the best choice.” Is this just a statistical effect (smaller dataset, higher noise) or is there something else afoot?
A: It’s more of a resolution issue. Think of it like a TV screen. The closer you get to the screen, the better you are able to make out an image. But get too close, and all you see are pixels.
Q: What’s an example of a factor that influences local real estate prices that might surprise an average person?
A: One thing that people don’t think about enough is the role commuting costs play in housing affordability in cities. When roads become congested, housing near the center of the city becomes more expensive as households compete to live closer to where they work. What that means is investment in better transportation networks can help make housing more affordable. ♠️