Housing Price Dispersion in a Cooling Market: Q4 2018

Posted January 7th, 2019

With the announcement of Amazon’s new headquarters, many have decried the behemoth’s decision to choose two already-prosperous cities, invoking arguments often used in denouncing the increasing inequality in the US today. And the rich do indeed get richer, not just in terms of Amazon HQs, with inequality that continues to be visible and ever-rising among Americans. With an awareness for this rising inequality, RealtyHop’s data science team has analyzed inequality within the realm of residential real estate prices.

To do so, we’ve looked into pricing dispersion among the top 20 most populated counties in the United States, which we define by taking the interquartile range of residential listing prices. Put more simply, this measures how large of a price gap there is in residential real estate for a given county.

In exploring the pricing dispersion of these areas, we’ve also paid close attention to the distribution of housing prices and how that has shifted over the quarter. As you’ll see below, areas where pricing dispersion has decreased are also coupled with decreases in the upper quartile of housing prices, suggesting that the luxury or higher-end housing market is cooling faster than the median/lower quartile.

Key Findings

  • The counties with the largest average pricing dispersion were, unsurprisingly in New York and California – New York County (which is the borough of Manhattan) leads with $2M of pricing dispersion. Kings County (also in NYC – Brooklyn) is at a distant second, with $718K of pricing dispersion. Close behind, however, is Santa Clara County in the Bay Area of California with $693K of pricing dispersion.
    • This indicates that the person purchasing a home around the top 25th price percentile of New York County will pay $2 million more than the bottom 25th percentile.
    • With the bottom 25th price percentile for New York County coming in around $649,000, one could buy 4 properties in the bottom 25th percentile for the same price as a property in the top 25th percentile.
  • In the past 3 months, the largest decreases in price dispersion have been in the tech-dominated areas of both Seattle (King County) and the Bay Area (Santa Clara), with 29.1% drop and a 27.3% drop respectively.
    • This corresponds to a $215K (Bay Area) and $110K (Seattle) drop in price dispersion, which indicates that the prices of houses are converging and that housing price inequality in these areas is dropping.
    • That said, the top 75th percentile of listed housing prices has also dropped noticeably in both Seattle and the Bay Area, which is driving the majority of the decrease in price dispersion.
    • This decrease in the top 75th percentile of listed housing prices is likely caused by some combination of increased interest rates, market corrections in the tech sector, and the various nuances of the housing markets in the areas in question.
  • Housing Price Dispersion

    We’ve combed through RealtyHop’s proprietary data to build a comprehensive index of housing dispersion in a number of different counties, looking to identify the interquartile range between housing prices. By doing so, we capture the price gap between housing stock across the country. A higher price dispersion number suggests a higher amount of inequality, indicating a positive relationship between the two. With this in mind, however, singular data points should be taken in context, and conclusions should only be drawn from clear trends and not one-off peaks or troughs.

    See the below table for the average price dispersion looks over the 3 month period (from Oct 2018 to the end of Dec 2018):

    Infographic

    Use the interactive widget below to see how different counties compare– the below graph will show the 25th percentile, median, and 75th percentile housing price in a given county. The pricing dispersion is the gap between the 75th percentile and the 25th percentile (the orange and the blue line, respectively). You can filter by any of the top 20 counties.

    Data Considerations

  • The housing price data is RealtyHop’s proprietary data, and the population data comes from the US Census.
  • We are looking at the original asking prices of properties to calculate the price dispersion, and this only includes residential properties that are on the market. While we believe that this closely follows the true value of properties in a given county, there is some selection bias at play.
  • While many people have a large majority of their net worth in their home, the incidence of home ownership is very much a function of geography. For example, home ownership rates in New York City and the San Francisco Bay Area will be drastically lower than those in the Las Vegas area (Clark County).
  • With the number of days on market falling, the above index may be subject to mix considerations– with a cooling real estate market and houses being purchased faster, there may be quick market shifts among certain price points.
  • What Does This Mean For You?

    On its own, the pricing dispersion for a given county is more descriptive than prescriptive– for those looking to purchase a home (either for a primary residence or for investment purposes), however, understanding how prices skew in a given county is incredibly important. For example, if in a given county there’s a lot of affordable housing construction, one may feel differently about purchasing a house within the lower band of price percentiles. As said housing stock becomes more commonplace, it may prove to be a less attractive investment.

    Appendix: By-Month Data Table

    The data table below shows the past 3 months worth of housing prices by county, the pricing dispersion by month, and the census population associated with said county.