Data detailing the kinds of properties for sale in your neighborhood and their selling prices can be valuable to realtors, homeowners, house hunters, and real estate investors.
With the help of some real estate analytics tools, savvy agents and smart buyers can transform this data into real-time visualizations, suggesting list prices for homes going on the market, how much a house in a particular neighborhood is worth, which communities might most attract real estate investment, and more.
The data comes from the Multiple Listing System (MLS), a national database of properties on the market or under contract to sell. The MLS database includes information on properties in foreclosure or short sale, the size and type of properties for sale, average and median property prices, and even amount of time listed, for example.
With the MLS data as a starting point,Brian Block, a managing broker and realtor at RE/MAX Allegiance, in McLean, Va., who has an analytics background, said he always crunches the numbers to provide clients with as much data as possible.
“I’ve always used this kind of data in my marketing,” Block said in a recent phone conversation.
Using an analytics tool called RealEstate Business Intelligence, Block can transform local community real estate data into graphs showing marketing activity, median sales price, average number of days on the market, and more, for any of the local communities where he sells property.
“I think clients are really impressed when I come in and start pulling graphs off my iPad,” he said.
Using this analytics tool, Block can even generate heat maps of various regions showing local real estate activity, including areas with heavy foreclosures or where properties have remained on the market for more than 60 days.
He can drill down to individual ZIP codes, school districts, and even subdivisions for a look at real estate conditions on the local level. What’s great is that the tool allows him to introduce a reality check into conversations with clients who read real estate prices quoted in The Washington Post, the largest local metropolitan newspaper, and get an inflated view of local land value, Block said.
Despite availability, surprisingly few real estate agents use robust analytics tools, Block said. He estimated only between 5 percent and 10 percent of his competitors do so, for example.
Have you bought or sold a home lately and, if so, did your agent use analytics in the process? Let us know in the comment section below.
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I'm the product manager over at RealEstate Business Intelligence - just happened upon this post and we're obviously thrilled to see the www.rbintel.com product summarized. Cordell, regarding your comment on the fact that different property characteristics impact the trends, we're in the prototype phase for a new feature that will allow for multi-factor filtering (e.g. Show me the median sales price for detached homes built between 1970-1975 in X Zip Code) that will make the stats even more relevant to the individual buy/sell scenario. A bit leery folks getting too granular with their filters (and hence getting too small a sample size for ample trending), but we're going to put in some controls and messaging to help agents understand associated pitfalls. BTW, love this site, glad I stumbled upon it!
Cordell it an excellent point I live in one neighborhood but my zip code is another neighborhood none of the models detect this and price my house according to zip not location....it's a common issue where I live.
Shawn I did buy and sell a home a few years ago and I was surprised at how few realtors used analytics for the process. The provided the standard comp data for pricing but did little else with web analytics didn't measure website visits for property pages etc. It's really a lost opportunity. Even those that measured something could not get to more granular analytics.
Well theoretically I guess that's true but it seems inefficient to me. The price is what we agree on but your perception of what makes up that price may be wildly different that my perception. As long as we agree, fine but there's no way to really tell if you're over or underpaying. I'm thinking of the bidding wars during the boom. Pricing had lost any connection reality. Yes it was driven by demand but that demand was mostly only a perception in buyers minds not a reflection of reality - or prices would have stayed up!
I guess what I'm suggesting is that Block's tool is creating averages and projections based on what's really occurring in the market. So, wouldn't the quality of the school district and the low crime rate already be calculated in? Isn't that the reason people are paying what they are?
I'm not a real estate expert Shawn but I think those items are only reflected in the price indirectly. What's a great school district worth? $10k, $20K? Right now it's just a wild guess. Yes, I might argue that the attributes are worth more or I might decline the offer, especially if I have a something more substantial to back up my pricing. If course supply and demand will be the biggest drivers. Having a model would allow for more granularity and tranparency and a way to quantify what various attributes are worth to the market.
I think the most important aspect here, as I suggested to Cordell, is that these prices are realistic in that they are based on real-time buying and selling of properties going on in your local community.
I guess my question would be, if this tool is measuring how long houses stay on the market and how much they sell for, how much more data should you collect? The things you're discussing (school district, crime, parks, whatever), aren't these factors already reflected in going prices. To state my question another way, if an offer is made on your home that is, say, $20,000 less than you would like to get, but is right in line with all the other properties in the area, what are you going to do, talk about the great school district, the great recreation, and the low crime? If your potential buyer already knows what the going rates for all the other houses in the neighborhood are, why would he pay you an extra $20,000 for the house.
At any rate, this type of data is great starting point. giving homeowners, real estate investors and brokers a starting point. It is data that brings information to the negotiating table, and offers a better hand to the informed buyer or seller.
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