Real estate statistics are often used by brokers, buyers and sellers to guide important decisions regarding the sale or purchase of property. However, several commonly used real estate statistics are usually applied incorrectly or are so flawed that they should be avoided completely. Here are examples of three real estate statistics that should be avoided when analyzing market conditions, comparing property values or considering terms of a real estate sale.
1. Price Per Square Foot: This value is used by insurance companies to estimate the replacement cost of properties, usually just the improvements and not the land. Contractors use it to bid construction or remodeling projects. However, it is also used frequently – and incorrectly – to compare the value of two or more properties. Price Per Square Foot fails to take into account location, view, neighborhood influences, lot size, allocation of square footage for specific use within the living space, exterior improvements and distinctive features that add or detract value.
2. Listing Price Versus Sale Price: A common misconception is that sellers usually set their asking price close to market value. That is just not always true. There are many pricing strategies that brokers can recommend that make Listing Price Versus Sale Price a worthless statistic. Here are two.
Asking Price Above Market Value: Sellers may set their asking price well above market value when they believe prices are trending upwards or current demand for a property is strong. The underlying strategy is to “pull offers up.” Setting the price high provides negotiating room, if required, and also attempts to establish an assumption that the property is worth more than it actually is.
Asking Price Below Market Value: Asking prices are typically set below market value when the seller desires a quick sale or if the property has a significant flaw. But the low ball asking price strategy is once again becoming common in many parts of the U.S. The objective is to attract as many offers as possible by asking a very low price, then create a bidding war that drives the eventual sale price as high as possible. When successful, the end result can be a sale price above market value. When it fails, the seller may receive many bids around the asking price, which is well below an acceptable sale price.
Either of the above strategies can widen the gap between listing price versus sale price regardless of market conditions, therefore making any measurement of the difference between prices meaningless.
3. Average (Mean) or Median Days On Market: Studies indicate that homes sold after they have been on the market a long time usually sell for less than homes sold quickly. However, to characterize market conditions based on the average or median days required to obtain an acceptable offer could be misleading. Especially when there are only a small number of sales in the market. For example, let’s look at sales in a subdivision during a single month as shown in the box to the right. The average Days On Market (DOM) before receiving an acceptable offer for these sales was 35 days. The median DOM was 15 days. Does either number provide a good answer to this question:
Q: “Mr. Broker, I’m thinking of selling my home. How long is it taking for homes like mine to sell?”
A: “Mr. Seller, the average number of days that it takes to obtain an acceptable offer is currently 35.”
Should Mr. Seller expect an offer near the average DOM? Probably not. Over half of recent sales – 64% – sold in less than 30 days. Only 18% sold between 30 and 60 days and 18% sold in greater than 90 days. Based on sales in those time frames, Mr. Seller might be interested in knowing that a sale in less than 30 days would be 3 times more likely than a sale in 30 to 60 days or a sale in greater than 90 days.
Another concern with making decisions based on a small population of data is the influence of one or two data points that disproportionately skew market statistics. In our example, if Homes 10 and 11 did not sell, the average DOM would be 19. Those two homes represent just 18% of all homes that sold, but they increase the average DOM about 84%.
Finally, there is no way of measuring how comparable the homes are that sold. Some may be in vastly better (or worse) condition or have superior locations or features, such as an ocean view, which could drastically reduce DOM while driving up the price.
Most real estate statistics are top down measurements, even though a bottom up analysis is usually a more accurate approach to reaching a reliable conclusion. These three real estate statistics are based on questionable data, therefore should be avoided when characterizing market conditions or comparing values of properties.
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