The Woeful State of Market Statistics on (Some) Broker Websites

At RMU, we create charts and statistics on local real estate markets. Where it’s available, we use data from the Multiple Listing Services (MLS) that the Realtors use.  The vast majority of transactions go through the MLS systems.

Behind the scenes, the process of turning raw MLS listing data into meaningful charts and statistics is a bit complex.  Unfortunately, a lot of people get important parts of it wrong … sometimes very wrong.

Several times a year, we check our numbers against those from other sources.  And we ask clients who are members of the MLSs involved to run searches through the MLS to verify the numbers.

The results of a recent mini-survey are below.  The numbers are real.  The names are not.  What it shows is that on different broker websites (with names you would recognize), unit sales for single family homes in the same town in the same time period … are radically different.

Single Family Unit Sales in Q3, 2010 as shown in December, 2010
			———— Broker Websites ————
Town	Best MLS for the Town	MLS Count	Big National Realty	Upscale Realty	Independent Realty
Mayberry	County MLS	166	476	165	166
Springfield	State MLS	51	198	2*	51
South Fork	State MLS	17	46	2*	17
Wisteria	Wisteria MLS	141	375	34	141

* Upscale seems to have stopped updating State MLS after July

What’s going on with ‘Big National Realty’?  Their numbers are roughly 3 times what the MLS reports.  Could it be that an under-paid, under-qualified clerk somewhere thought that to get the quarterly number you had to multiply the monthly totals by 3?  And no manager or QC person looked at the results?  Don’t know … just speculation.

And then ‘Upscale Realty’ … they pretty much nailed County MLS.  Good job.  But they haven’t updated State MLS since July.

But then what’s going on with the town of Wisteria. Wisteria has their own MLS.  It has all the listings.  Some of the Wisteria listings wind up on County MLS … roughly 20%.  So it’s a pretty good bet that ‘Upscale’ is taking Wisteria listings from County MLS when they should be taking them from Wisteria MLS.

Independent Realty gets it right.  OK, RMU does their numbers.  Part of our process is to have data from a sample of towns checked back against the MLS.  If anything doesn’t match, we find and fix the problem.

I don’t think it does anyone in the business any good when a buyer or seller can go to three prominent broker websites and see that sales in a town last quarter were either 375, 34, or 141.

The most sensible place to regulate this is with the MLSs.  In each geography, the vast bulk of web traffic goes to a few brokers.  In less than an hour, somebody at the MLS could look up the sales in a few towns and check the stats on the dominant broker sites.  Do this, say, at the end of each quarter since a lot of these reports are quarterly.  If there’s an issue, have the offending party take down those pages until it’s fixed.

Use Market Data to Create Interesting Blog Posts

Let me make some suggestions on how to use data and charts in your blog or newsletter to make it interesting and differentiate yourself:

1. Do ranking lists.  Everybody loves keeping score.  Name the 10 highest/lowest price towns in the county.  Has it changed since last year? … five years ago?  Which towns have depreciated least/most in price since the peak? (then use your knowledge to explain why).  There are many possibilities.
2. Compare what’s going on in different price ranges.  Most of the charts out there cover the market as a whole.  Different price segments may not all be behaving the same way.  Pick a couple of segments and show and explain the differences.
3. Compare towns.  Pick several similar towns you cover.  How have they performed in terms of price and sales over the past several years?  Which has the best values now?
4. Compare property types.  How are single families performing compared with condos?
5. Use the data and charts to explain a narrative.  Let’s say you’ve heard about a couple of recent deals where people moved from Orinda to Redwood Heights.  Might be a trend, might not be.  Look at charts of inventory and number of new listings in Orinda.  Are they going up?  Check trends in sales and prices in Redwood Heights.  Are they stable or improving?  In other words, come up with factual evidence to support your ideas.  You’ll be much more convincing.
6. Everybody’s interested in the rich.  Pick the wealthier towns in your area.  Are they closer in price than they used to be or further apart?  Explain why you think this is.
7. Educate your audience.  Introduce people to a statistic that’s important, but that they might not be familiar with.  Consider Months of Supply or Sales-to-List Price Ratio.  Define it and then explain what it means and how it applies in your area.
8. What are values really doing?  Average and median price don’t tell the whole story.  Look at price / sq ft as well.  Compare historical sales prices of some typical properties.

[Anywhere above you see the words ‘price’ or ‘sales’, you can substitute ‘inventory’ or ‘market time’ or ‘months of supply’ or ‘new listings’.]

The beauty of this is that all these ideas can be used again and again.  Doing the work the first time is the hardest part.  But I guarantee you’ll learn something useful about your market.  And once you’ve done one … others are much easier.  And you can basically write the same column six months later … and it will be different because the data is different.

For (free!)  information on how to download and use data from your MLS, see http://bit.ly/epNz5Z

And … RMU has tools that save time and make all this easier … www.RealtyMarketUpdate.com

Thanks to Brian Boero of 1000WattConsulting for inspiring this

Is the First Offer the Best?

This post was originally motivated by this video by the Freakonomics guys (courtesy of Jack Lindberg on LinkedIn).

The basic claim on the video is that the interests of the seller and Realtor are not well-aligned.  If you’re selling, the Realtor will try to get you to take the first offer, even if it’s a little low when you may be better off waiting for a higher offer.  If an offer for $10K more were to come in a week later, you would get $10K more but, after all the splits, the Realtor would get a pittance.

The “evidence” for this is that, when Realtors sell their own homes, average market time is 10 days higher than when selling a client’s.  They also claim that when Realtors sell their own homes “they get more money than when they sell the same home for a client”.   (Don’t understand what evidence could possibly support this statement.  To get an apples-to-apples comparison, the Realtor and client would have to try to sell the same home at the same time.  Freaky.)

I can’t think of a direct way to test their theory directly.  But if it’s true, you would expect the sales / list price ratio to go up with market time, at least for a while.  That is, sales with very short market times, where the sellers presumably leapt at the first offer, would not do as well as sellers who waited for a better one (all other things being equal).

We all understand that there are many important factors that are not accounted for here.  For example, was the house priced reasonably to begin with?

Leaving those aside for now, what do the numbers tell us about the relationship between market time and sales / list ratio?  The chart below is based on around 6,000 sales in Fairfield County, CT in the past 12 months.

The scattered dots represent the sales / list ratio for the given number of days on market.  For example,  properties with a market time of 100 days, had a sales / list ratio of 94%.

The thin black line curving from the lower left to upper right is the cumulative % Sold.  So, for example, just under 60% of the sales took place within 100 days of listing.

(Note: The sales/list ratio is a 7-day moving average.)

As you can see, the sales/list ratio drops from the 99% vicinity to around 94% in the first 80 days. From there it decreases more slowly, getting to the 92% area after 300 days (when 90%+ of properties have sold).  That is, longer market time, lower sales/list ratio.  At least in the type of market we’ve been in for the past year.

You might argue that things would be different in an up market.  If you look at the chart below from better times in 2004, there’s a difference in degree, not in kind.  The curves basically shift up and to the left, meaning that sales/list stays a couple of points higher (dropping from above 101% to 97% in the first 70 days, then down to 96% after 180 days (when 90% have been sold).  And you get to 90% sold in about 170 days as opposed to 260.

In 2008 and 2009, when the market was going down quickly, the same patterns are there.

Anecdotal evidence would suggest that lots of people list their houses a little above the market.  I think most of us have been through this discussion.  We’ll go in a little high and maybe someone is out there who will fall in love with the place and pay the price.  If this someone doesn’t come along, we’ll drop the price.  After 90 days, you’ve dropped 5-6%, which is probably what you think it really should sell for, so you hold there if it’s still on the market.

There’s also what I would call the “new listing splash factor”.  That is, when a listing is new, lots of people see it at once.  After that, it’s just a trickle.  So when the property is first listed, you’re hitting the most people in the shortest amount of time.

If I were trying to explain all this to a seller, I think I would say that if you want to sell in the 60 to 90 day timeframe for 95% of asking price or better, you should price realistically to begin with.  If you want to go in a little high, fine, but be prepared to cut quickly.  If you hold out, you’re going to be on the market longer and likely have to drop the price anyway.  Even in a “good” market.

Data is from the Fairfield County Consolidated Multiple Listing Service, used with the permission of New Era Realty, Danbury, CT, a licensed broker in CT and CMLS member.

More information at www.RealtyMarketUpdate.com (or www.RltyMktUpd.com)

Commuter Compass Launch

I’m pretty excited about the launch of our new search product Commuter Compass.  The idea came from some of my own real estate buying experiences.  Starting with a price range in mind, where to buy was basically a tradeoff between how much space I wanted and how far I was willing to commute.  At the beginning of the process, had to do a lot of looking in a lot of different towns to get an idea of the possibilities. 

 Commuter Compass brings together market information on all the towns that might work for you in one place at one time.  Much simpler than doing searches on 5 or 10 or 20 different towns and putting it together yourself.  It helps you to narrow down the possibilities and home in on towns that are most likely to have the best choices. 

 When I had the first prototype working, I thought back to the first house I bought.  At the time, I was working in Stamford, CT.  The place I actually bought was in Trumbull, about 25 miles from work (45 minute drive).  I input Stamford and a maximum commute of 25 miles and ran it. 

 Looking at the results was striking.  I discovered another town, Ridgefield, further to the north, but 6 miles closer to Stamford.  Ridgefield is a little more expensive than Trumbull and for the same price, I would have gotten a slightly smaller house.  But that was a tradeoff I would have made … if I had known about it.  At the time, I had the impression that Ridgefield was too expensive and never bothered to look there. 

I think a lot of people might find themselves in a similar situation at some point.  So I hope Commuter Compass can help them make better decisions.  It would have helped me.

Commuter Compass Demo Video

RealtyMarketUpdate.com 

Measuring Market Time

A question that frequently comes up is how to measure market time.  The two main options are: (1) measure from the date of the most recent listing (2) if a property has been listed more than once (by the same or several agents) and the listings are close in time, then market time should be from the beginning of the first listing.  I refer to this as “chaining” the listings together.  Which is better? 

There are arguments for both options.  The argument for chaining is that some agents will deliberately overprice a listing and then re-list at a lower price.  Re-listing, as opposed to doing a price reduction, will reset market time for the property back to zero in most MLS systems. 

Some agents will price high just to get the listing.  This  is generally frowned upon, at least in part because if the property is not priced properly to begin with, it’s likely to be on the market longer before selling. 

So scrupulous, professional agents prefer to measure market time via chaining because it shows up the people who are “behaving badly” … that is, it reflects the longer time the property spent on the market due to improper pricing. 

On the other hand, when studying the market, we look at market time as an indicator of the balance between supply and demand.  We’re not really interested in measuring bad agent behavior or unrealistic seller expectations.  So if an agent priced a property too high and then relisted at a lower price, buyers were most likely unaware of the price on the initial listing. 

So which is better?  The answer is it depends on what you’re going to use the market time number for.  If you’re trying to decide which agent is better, you want to consider the full market time of the chained listings.  But if you’re trying to figure out the balance of supply and demand, better to use the latest listing only.  That’s the only one the buyer saw.