What direction is your market looking?

U.S. homebuyers are finding fewer properties that fit their budget, and in some markets, the majority of available homes may be too expensive given the current demand, a new analysis by Trulia suggests.

Today, we’re launching a new, semi-regular report called MarketMatch that gauges the price points home seekers are searching and compares them to the prices of available properties.

In our inaugural look, we found that nationally 10.4% of searches at a certain price point failed to match the available inventory at that price point. And in many markets – including many in Florida, North Carolina and Texas – many home seekers are disproportionately looking at homes priced much lower than available inventory.

When it comes to shopping for a home, buyers are at the mercy of the market. But, what they’re searching for is more a function of budget and tastes. If you are a prospective home buyer looking in the same price range as a bunch of other prospective buyers, but there are very few homes available in your market in that price range, you will have a frustrating time. On the flip side, if you are selling a home and have priced in a price range that very few people are interested in, you may be waiting for a while.

These sorts of mismatches in the market can drive up prices in certain popular price ranges or cause properties to sit on the market longer than they otherwise would. As discussed in Trulia’s quarterly inventory report, many metros are tight on starter homes at the moment and heavy on higher-end homes. This puts upward pressure on starter home prices, especially in metros where people seem most interested in lower price ranges.

As peak home buying season wraps up across much of the country, we look at how well individual markets match up with search interest. We categorized metros into three groups:

  1. Places where there’s a disproportionate amount of searches for homes are in lower price ranges relative to what is available;
  2. Places that are right on the money, where listing prices and search interest match up well;
  3. And places with rich tastes (or lousy housing stock) where people are disproportionately searching for homes at higher price points relative to what is available.

Looking at all active listings and the number of times those listings were viewed nationally and in the 100 largest U.S. metros from March 15 to Sept. 15 we found:

  • Nationally, the mismatch rate is 10.4% between the distribution of home prices that users are viewing and the distribution of prices of all listings, what we call “market mismatch.” This compares with a market mismatch rate of 8.3% during the same 6-month period in 2015.
  • 53.4% of all properties viewed nationally were priced below the median list price compared with 51.0% during the same period in 2015.
  • Houston and Dallas have the highest and second highest market mismatch scores of 31.2% and 30.3%, respectively. As market mismatch approaches 0%, you would expect the share of searches that go to homes below the median to approach 50% (half the homes would receive half the search activity). A full three-quarters of all search activity occurred on houses that were priced below the median listing price in both these metros though.
  • Buyers in only 10 of the 100 metros looked at more expensive homes than were mostly available. These are places where users are showing disproportionate interest in higher priced homes. This list includes Detroit, Philadelphia, and Dayton, Ohio, where only 39.7%, 42.2%, and 44.8% of searches are for homes that are less than the median listing price.
  • Honolulu, New Orleans, and Albuquerque, N.M., have the lowest market mismatch scores of 6.2%, 6.9%, and 7.0%, respectively.
  • Miami saw the largest increase in market mismatch going from 8.6% in 2015 to 21.2% during the same period in 2016. Colorado Springs, Colo., on the other hand saw the largest decrease market mismatch going from 28.6% in 2015 to 16.1% in 2016.

First, let’s take a closer look at the places with the highest proportion of searches being done in the lower half of all listings during that time. While Houston leads this group with the highest market mismatch score of 31.2%, this is actually a slight improvement from 2015 when the market mismatch was 32.1%. With a median listings price of $334,950 during the 6-months ending Sept. 15, 75.5% of all properties viewed in Houston were homes below this price. Of the six Texas metros in the analysis, five of them are in the bottom quarter of market match scores in 2016 (El Paso being the exception). Also making this list are Charlotte and Raleigh, N.C., and North Port, Fla., all with more than 69% of search interest being directed toward the bottom half of listings by price, all with market mismatch scores over 23% and higher than their respective 2015 scores.

Top 10 Value-Focused Markets

Metro Area 2015 Visits Below Median List Price 2016 Visits Below Median List Price 2016 Difference From Balanced (50%) Market Mismatch 2015 Market Mismatch 2016 Change in Market Mismatch
Houston, TX 75.0% 75.5% 25.5% 32.1% 31.2% -0.8%
Dallas, TX 73.1% 74.3% 24.3% 29.4% 30.3% 0.8%
Deltona-Daytona Beach-Ormond Beach, FL 69.7% 73.4% 23.4% 19.0% 19.7% 0.8%
Fort Worth, TX 67.3% 73.2% 23.2% 18.9% 20.7% 1.7%
Charlotte, NC 64.7% 71.9% 21.9% 16.4% 23.4% 7.0%
Cape Coral-Fort Myers, FL 71.5% 71.5% 21.5% 24.0% 21.0% -3.1%
Greenville, SC 66.5% 70.8% 20.8% 19.1% 19.4% 0.2%
Raleigh, NC 64.2% 70.0% 20.0% 17.2% 24.8% 7.6%
Grand Rapids, MI 62.5% 69.8% 19.8% 10.0% 17.4% 7.4%
North Port-Sarasota-Bradenton, FL 70.3% 69.7% 19.7% 19.9% 24.1% 4.1%

 

Why Texas Cities Rank So High For Mismatches?

Pinning down exactly why a market would have mismatched search interest relative to available listings comes down to local market dynamics. In the case of Houston (and much of Texas), year-over-year job growth has gone from 3.6% in early 2015 to 0.6% today, partially as a result of oil prices tumbling more than 50% in the latter half of 2014. With job growth now well below the national average of 1.9% and prospects more limited, shoppers are likely taking a more conservative approach to their home search. In Dallas, however, job growth accelerated from early 2015 to today, yet the mismatch is large and growing when compared with the same period in 2015. Unlike Houston which was fairly flat, the median listing price and price per square foot in Dallas climbed 11.8% and 10.4% year over year in August 2016, while search interest remained relatively unchanged, further driving a wedge between interest and listings.

Top 10 Matched Metros 2016

Metro Area 2015 Visits Below Median List Price 2016 Visits Below Median List Price 2016 Difference From Balanced (50%) Market Mismatch 2015 Market Mismatch 2016 Change in Market Mismatch
Honolulu, HI 50.3% 54.7% 4.7% 4.3% 6.2% 1.8%
New Orleans, LA 42.9% 50.6% 0.6% 7.4% 6.9% -0.6%
Albuquerque, NM 56.4% 58.2% 8.2% 7.4% 7.0% -0.4%
Silver Spring-Frederick-Rockville, MD 54.9% 56.7% 6.7% 4.6% 7.3% 2.6%
Newark, NJ 45.4% 49.8% -0.2% 5.8% 7.8% 2.0%
Little Rock, AR 50.4% 49.8% -0.2% 5.9% 8.0% 2.1%
Virginia Beach-Norfolk, VA 57.1% 57.3% 7.3% 7.0% 8.1% 1.1%
Gary, IN 51.6% 47.5% -2.5% 4.2% 8.1% 3.9%
Madison, WI 58.1% 61.8% 11.8% 8.8% 8.2% -0.6%
Baton Rouge, LA 56.0% 59.8% 9.8% 5.1% 8.6% 3.5%

If you are a house hunter or seller in any of our top 10 Market Match metros, odds are you found your experience less frustrating than someone in a mismatched market. In New Orleans, for example, where the market mismatch was 6.9%, almost exactly half of all properties viewed were below the median listing of $236,000. Buyers looking for homes in the popular $150,000 and $250,000 range find this to be the most popular price range for people listings their homes. Baton Rouge also makes this list coming in at number 10 with a market mismatch score of 8.6%. There is a much clearer lower price preference in overall search interest in Baton Rouge, La., though with 59.8% of all search activity being done below the median listing price of $219,900 during this time.

Where Searchers Look High in Cities Full of Mediums to Lows

Finally, there are the handful of places where search interest disproportionately falls on the high-end of the listing price spectrum. The market mismatch scores for these places are relatively low, in the mid-10% to high single-digit range, but these markets are unique in that Trulia users are disproportionately showing interest in listings at price points above the median listing price recorded during peak home buying season. In addition to Detroit, Philadelphia, and Dayton mentioned above, Camden, N.J., Toledo, Ohio, and Gary, Ind., also make this list. In addition to being among the most inexpensive markets among the top 100 metros, population decline as a result of net out-migration, and the low quality of available housing stock is a common theme among all of these places.

10 Metros With High-End Shoppers

Metro Area 2015 Visits Below Median List Price 2016 Visits Below Median List Price 2016 Difference From Balanced (50%) Market Mismatch 2015 Market Mismatch 2016 Change in Market Mismatch
Detroit, MI 41.9% 39.7% -10.3% 7.8% 16.2% 8.4%
Philadelphia, PA 41.2% 42.2% -7.8% 6.4% 11.4% 5.0%
Dayton, OH 44.4% 44.8% -5.2% 5.3% 9.4% 4.1%
Akron, OH 45.4% 45.6% -4.4% 4.6% 10.6% 6.1%
Camden, NJ 46.1% 46.1% -3.9% 5.2% 16.3% 11.1%
Toledo, OH 44.5% 47.3% -2.7% 6.3% 14.5% 8.2%
Gary, IN 51.6% 47.5% -2.5% 4.2% 8.1% 3.9%
Pittsburgh, PA 47.4% 48.2% -1.8% 6.4% 11.9% 5.5%
Little Rock, AR 50.4% 49.8% -0.2% 5.9% 8.0% 2.1%
Newark, NJ 45.4% 49.8% -0.2% 5.8% 7.8% 2.0%

There are many factors that could be contributing to mismatched search trends relative to available listings including local economic factors and demographic trends, to just changing Trulia user demographics. As a longer history of search interest relative to listings becomes available going forward, how individual areas change over time and in response market forces will shed more light on how search and inventory patterns relate to pricing. During our next installment of this report, we will look more closely how these listing and search patterns fall into the starter, trade-up, and premium price ranges in each metro and what effects this may be having on shoppers and sellers in these tiers.

 

To download our full MarketMatch data set for the top 100 U.S. metros, click here

Methodology

All listings that were for sale (excluding pending and active contingent) between the dates of 3/15/2016 and 9/15/2016 were used for 2016 and 3/15/2015 and 9/15/2015 were used for 2015. The distribution of prices for each metro and nationally was calculated by taking each active listing and adding up the number of days it was on the market during this 6-month period. All unique price points were consolidated and counted by summing these day & price combinations. So if 10 properties were on the market for a combined 600 days at a price of $200,000, then the $200,000 price point would receive a weight of 600.

Site traffic was also matched to each unique property (and therefore price) on the market at this time and added up for the 6-month period.

Kernel densities were estimated for both listings and search interest using the weights described above. “Market Mismatch” scores were then calculated by measuring how much overlap exists between the two distributions in any given geography. So there is a distribution of listings by price and a distribution of search activity by price, each of which have an cumulative area of 1.0 or 100%. When calculating the area of overlap between the two distributions, if they were to line up perfectly, the market match would be 100% (the mismatch would be 0%). Any deviation from perfect match results in a score of less than 100%. This provides an overall measure of fit for a geography, but in order to determine the degree of price preference in searches relative to listings, we needed to also examine which way deviations from 100% match were skewed. Weighted median listing prices (weighted by the days available multiplied by the number of properties described available at that price, as described above) were then compared to web traffic weighted by the number of times each price point was viewed to come up with the proportion of views below the market median listing price.