I got a call from a real estate agent last week who asked me if the days on market figures in my market studies were based on the original list date or last list date (the last time the price was changed). It was something she wanted to have a better feel for when advising her clients on one of her listings. I directed her to my methodology page and had a pleasant discussion about the differences between the original and last listing dates in calculating days on market.

Ten years ago, this discussion would not have been part of the real estate conversation. In the late 1990’s, I started tracking listing inventory and the time it took to market a property in Manhattan. I had been kicking around the idea since the late 1980’s but couldn’t figure out how to capture the information. Listing databases in Manhattan didn’t gain widespread use until about 1989.

Here are the formulas that are used. Days on market (DOM) calculations are generally done two different ways:

• DOM From Original List Date – Measured from the date the listing was first placed on the market. The calculation is: Original List Date – Contract Date. This is often the easiest to measure but is less useful. For example, with that \$1,000,000 property I mentioned earlier – lets say the property was listed at \$2,000,000 yet worth \$1,000,000. It sits on the market for 2 years. Is it a competing listing to other \$1M properties? Is it actually in the market? No it isn’t.

• DOM From Last List Date – Measured from the last time the list price was changed, if ever. This is what I present in my market reports. The calculation is: Last List Date Change – Contract Date. This is the more useful of the two methods because it shows the market’s ability to absorb a property once it actually enters the market. Essentially, it is the list price of the property just before it goes to contract. In other words, it is the list price that brought the property into the correct market segment and attracted buyers.

It took a while for me to figure out how to collect this information, and to a certain extent, I have not yet figured it out completely. The problem is, the data I collect is top level and can’t be drilled down to a more granular level like “2-bedroom pre-war co-ops on the Upper West Side” yet. I am fairly confident that historical data does not exist anywhere.

Listing history of the immediate market ie, price reductions or increases, and their associated date, was something competent appraisers and agents should always consider when valuing a property.

The problem with listing related information, unlike sales information, is that it is very difficult to capture this information in its aggregate form. Why?

Looking at sales data for answers

If a property closed for \$1,000,000 today, I will capture the sales data and keep it in my database in perpetuity. A few years from now, I could look back at my database and see all the sales that closed for about this price on or near this date, or build a graph that shows price trends.

When I review active listings in a particular market as they relate to a property I am appraising, I look at the days on market before I consider finer property nuances like condition, layout, room count and so on. It provides clues to the upper limit to value, helping define the possible value range of a property whose value we are estimating. The same goes with sales that closed. I want to understand how the property was accepted in the market. Did it sell quickly? Did it languish?

If that same property was listed for \$1,000,000 today, the price might be reduced, raised, get taken off the market, re-listed with another agent, etc. Listing systems generally don’t have the ability to capture the snapshot of the market at a previous point in time, other than today. One property could have a half dozen prices associated with it and various dates. The list price tomorrow, if unchanged, would remain the same, however, a simple query on tomorrow’s date wouldn’t show the listing. The search would need to encompass what properties were listed on that day by looking at the listings with the same date and then looking back at each record to see if when the last price change was made.

This sounds fairly straightforward but this ability generally doesn’t exist in any listing database systems I have seen or read about. In my next market report release, which is imminent, I plan on analyzing the market by quintiles to simply see how the DOM changes in each market strata.