New UC San Diego Model Predicts Housing Prices to Fall as much as 18% this Year

Anew model of forecasting home prices based on consumer demand predicts that prices for housing will decrease by 5% nationally and 12% in San Diego County by the end of this year. The model, which highlights online search activity, was recently published in a new study from the University of California San Diego’s Rady School of Management.

The model’s predictions have a proven accuracy rate of up to 70% and are unique to other price predictors — such as Zillow, Goldman Sachs and Redfin —because those consider a variety of factors like interest rates, wage growth, unemployment and housing supply. Whereas the housing search index created by Allan Timmermann of the Rady School and collaborators at Arhus University in Denmark, focuses on consumer demand by tracking the rate at which prospective buyers use the internet to search for homes.

“It is one of the purest measures of potential demand that you can get because the first thing you do when you’re looking for a house or interested in buying a house, is to go to the internet and look at what is available,” said Timmermann, a distinguished professor of finance at the Rady School. “Those in the market for a home leave a big footprint with their online search activity because of the time it takes – often several months – to find something that is the right fit.”

Cities like San Diego have housing prices dropping more than the national average because it’s where the market overheated the most during the pandemic, Timmermann said.

“What you saw following the lockdowns in March 2020 was that sunshine and suburbs became a big thing,” Timmermann said. “People were shifting to working from home, so they wouldn’t have to be located close to the job and then they might cut out of their area altogether, choosing to live somewhere with more space and better weather. San Diego has plenty of suburbs and desirable weather, of course.”

These traits plus limited supply caused prices to skyrocket across the county, but the market has cooled by 2.5% since May of 2022 when prices peaked.

“Many households got priced out of the market so now we are seeing levels adjust,” Timmermann said.

But home prices in other cities are expected to fall even more. Phoenix, AZ, is predicted to have the biggest drop at 18%. Other metropolitan areas where prices are predicted to be on the decline include Stockton-Lodi, CA (down 13%), Las Vegas, NV (down 13%), followed by San Diego and Tucson, AZ. The cities with the most price stability include the metropolitan area of Scranton-Wilkes-Barre-Hazleton, PA and Kansas City, MO both predicted to rise by 2%. Other cities with forecasts of stable prices include Hartford, CT, Harrisburg, PA and Omaha, NE.

Timmermann added that the predictive power of internet searches tends to be a reliable indicator of where the market is going over the short-to-medium term as fluctuations in demand matter more than changes in supply, which tends to be quite stable over shorter horizons.

One major difference between the UC San Diego model for forecasting home prices and other, commercial price predictors is that the data underlying in the housing search index isn’t proprietary. The methodology is fully transparent and replicable as the study, published in Management Science, is public, so anyone can see how it works.

The formula starts with tracking key words such as “buying a house” and related search terms in Google Trends–a free website that analyzes the popularity of top search queries in Google Search. These data are compared to data on home tours and written offers, which allows the researchers to forecast prices in the short and long term.

“The cost of your time and the intensity with which you search and the number of people searching really does reflect the underlying interest in home buying,” Timmermann said. “At the end of the day, the higher the demand, the higher home prices will typically be.”

Coauthors of the Management Science paper include Stig Møller, Thomas Pedersen and Christian Schütte of at Arhus University.

The read the full paper, “Search and Predictability of Prices in the Housing Market,” go to this link.


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