Consumer Advocates...Not Sales People
 
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Area Covered

The links below provide access to housing data on the major communities of Boulder County, including Boulder, Louisville, Superior, Lafayette, Erie, Longmont, and Gunbarrel/Niwot. We also provide data on home sales for the city of Broomfield (Broomfield County) as well as Westminster and Arvada in northern Jefferson County. We do not include data on the foothills and mountains in our coverage area, nor on smaller communities such as Nederland or Lyons, because the comparatively small number of sales in these areas tend to make numerical summary data both meaningless and misleading. With the exception of the price appreciation data addressed under the heading Price Changes: Local vs US below, a printable version of this collection of charts is available here (Market Data in Printable PDF).

The stats on this site provide some unique and useful data on the local market. But please, if you're going to use them in thinking through your home purchase, take some time to scroll down and review our sections on how they differ from real estate data provided by other sources (e.g. The Boulder Area Realtor Association) and our cautionary comments on using statistical data in your home search.

Brief Comments on the Data in Each Section

  • Price Changes: Local vs US This is the only section in which we provide data that will allow you to compare the local market with the state and national market. These data will also provide a longer term perspective on changes in housing prices than you'll find in other data in this section. The web site of the OFHEO from which we've pulled these data will allow you to compare price appreciation in our areas in the past 30 years to appreciation rates in other parts of the country that you may be more familiar with.
  • Median Sale Prices The charts in this section will help you compare the prices of various sized homes, condos, and townhomes in the various communities we cover as well as get a perspective on changes in home prices going back as far as 1995. If you want to know what you're likely to pay for a 2,000 square foot home in Arvada or Boulder, or how much that home has appreciated in value over the past 10 years, this is the place to start. Like many others, we use median rather than average prices here. The median price is the mid-point price in an ordered list, so that if you order 400 home sales from lowest to highest price, the median is the price of the 200th home.
  • Percent Change in Prices These charts are based on the same data as those in the section titled Median Sale Prices, but here price changes are expressed as percentages rather than in absolute terms.
  • Number of Homes Sold These charts show the number of sales in each home size category, in each community, in each year. If you're looking for a house in the 900-1600 square foot size range in Longmont, these charts will tell you how many of those homes are selling each year. Shifts in the number of sales in a community, especially downward shifts, can also provide some insight into what's happening in that market. And if you're seeing wild fluctuation in sales prices or other data elsewhere in the charts, check here to make sure those data aren't based on only 10 or 20 sales.
  • Average Days on Market These charts report the average number of days a home is on the market before the seller receives and accepts and offer to buy it. Generally, these stats are used to track changes in the pressure on the market. If the average DOM shifts from 60 days to 90 days, sellers are having a more difficult time finding buyers and may be more open to price negotiations to get their home sold. In reporting real estate statistics, DOM stats are generally reported as averages rather than medians. We've done the same here, but we would note that the median DOM generally runs from 60% to 80% of the average DOM. If you want to know how long it takes half of the homes in a given market to go under contract, multiply the average DOM reported here or elsewhere by 70% and you'll have a pretty good estimate.
  • Sold in 2 Weeks or Less If you're a buyer, it's much less important to know how quickly the average house is selling than to know how quickly the homes that are the best values are selling. In these charts, we provide data on what percentage of homes are going under contract in 14 days or less. If you're buying in a market where 30% or 40% or 50% are going under contract this quickly, you need to act much more aggressively than you would in a market where only 5% or 10% go under contract within 2 weeks of hitting the MLS. These stats are probably the best measure of whether you're in a market that favors buyers or sellers.
  • Sold in 5 Days or Less These charts provide data on the percentage of homes going under contract within 5 days after they are entered into the MLS system. In most cases, these are homes that the buyer looked at on the first or second day after it hit the market and submitted an immediate offer. If you're buying in a market where 10% or 20% of the best homes are going under contract this quickly, you're going to have to look at houses the day they hit the market and be prepared to make an offer.

Using Stats in Making Buying Decisions

You can spend a lifetime analyzing real estate statistics and still remained baffled by some of the dynamics of the real estate market. Still, it can often be very useful for home buyers to spend an hour or two looking a statistical data on the real estate market in which they are planning to buy. Stats can give you a sense of whether prices are rising or falling, what communities you can afford to buy in, and whether properties are selling quickly. They can also give you an objective basis for evaluating claims about whether you're in a buyer's market or a seller's market.

But to give fair warning, studying stats can also help you make bad decisions. For example, many buyers want to use stats on price appreciation in various communities as a basis for deciding whether they should be buying a home and where they should buy it. While you shouldn't ignore sales stats in making these decisions, you need to remember that "past performance does not guarantee future returns." Stats are historical data and shouldn't be used to try to predict the future, at least not in isolation from other information about the community and market.

Technical Comments on Our Real Estate Statistics

With those caveats covered, you should take look at the stats compiled by the Boulder Area Realtor Association (BARA), in addition to the stats on our site. You can access several years' of their monthly statistical reports by selecting "Sales Statistics" from the list in the upper right corner of their home page.

Since our stats and those provided by BARA cover many of the same communities and use the same MLS as their data source, the same general trends will be reflected in both. However, we do use different subsets of this MLS sales data, and we use it in different ways. Some important points of explanation and clarification:

  • Both set of stats are based on sales data for properties that were listed and sold through the MLS database system used by Realtors. Data from properties that were not "listed" by real estate agents, but were sold independently "by owner" will not be reflected in these data. Still, because about 90% of sales occur through the MLS system, these data do provide a good picture of the market.
  • While both sets of stats cover the Boulder County market, there are some differences in how the parts of the county are covered. On the one hand, the BARA stats cover the mountain and plains areas outside the major cities. Ours don't because we believe the numbers generated are often misleading. There are too few sales and too many factors such as location and views that dramatically affect both sales price and time on the market in a manner. This makes it difficult to produce meaningful data in these areas.
  • While the BARA stats include sales of both new homes and resale homes, we limit our coverage to resale homes only. There are good arguments for either approach, but we feel that merging the two data sets is misleading. For example, builders often list new homes for sale when they get their building permit approved. Construction may not begin for a month, and it may be 3-6 months before construction is completed. This can significantly impact "average days on market" data which is intended to indicate how long seller's have to wait before they get contracts on their properties. As a result, "days on market" data for a city like Longmont, where there is a lot of new construction, may be significantly distorted.
  • The BARA stats not only lump together new homes and resale homes, they also lump together all homes regardless of size. There are reasonable arguments for taking this approach. Consider, however:
    • If the average size of Lafayette homes has changed over the past decade -- which is has -- historical data on price increases may exaggerate the amount that the price of an average 1500 square foot home has increased.
    • If the average home sizes home in Louisville and Superior differ -- which they do -- you may get a distorted picture of comparative home prices in Louisville and Superior if you use the BARA stats
  • To deal with this, our stats focus on classes of homes and condos that are defined by square footage. Our stats for "larger homes" reflect data on resale homes between 2300-3000 square feet in size, while "medium homes are 1600-2300 square feet and "smaller homes" are 900-1600 square feet. We limit our data on condos and townhomes to those that are between 800 and 1600 square feet in size. In defining each of these home sizes, we've focused solely on "above grade" living space square footage, ignoring the square footage in basements and garages. This makes it possible to compare sale prices of 800-1600 square foot homes in Boulder vs. Louisville, or to look at the rates at which prices have appreciated in a given community since 1995. Because these data allow you to compare "apples to apples," we feel that they provide a better picture of what is happening in the market.
  • Importantly, our stats provide data on the speed at which properties sell that is not provided in the BARA stats. Both sets of stats provide data on the average number of days that homes are on the market before the seller accepts an offer. However, we feel its important for our clients to know not only how quickly the average house sells, but how quickly the best properties sell. That's why we have data on what percentage of properties go under contract in 14 days or less and what percentage go under contract in 5 days or less. As a buyer, you just have to behave differently in a market where 25% of properties are going under contract in 5 days than in a market where only 5% are.
  • The BARA stats are updated monthly. Our's are updated quarterly.