USING THE RESIDENTIAL REAL ESTATE DATA BASE

By W H Inmon

Because real estate transactions are in the form of text, for years it was prohibitively expensive to consider building a residential real estate data base. But now with Forest Rim Technology’s textual ETL it is quite possible to build a residential real estate data base.

Supposing you did create a residential real estate data base. How could it be used?

The simplest way to use a residential real estate data base is to look at sale prices. If you looked at nothing else, looking at the closing price of the sale of a home is a very useful piece of information.

However, every piece of property is different. (Even different condominium units in the same condominium have differences.) So, looking at just sales price can be misleading. It doesn’t make sense to compare a small, older home with a new modern home, in most cases. The older home will have more wear and tear and the older home will be built in a different, older style than the newer modern home.

So, capturing the age of the home in the data base is a useful thing to do.

Another very basic piece of information in the data base is the square footage of the residence. Once the square footage is captured, calculating and comparing the price of a home on a square footage basis is a useful thing to do. The price per square foot is much more meaningful than the raw sale price of the property.

Another useful type of information that can be captured in a data base are any distinguishing features of the home. Does the home sit on a corner lot? Is the home on a busy thoroughfare? Is the home a ranch home or multi storied? Does the yard have special features?

All of these intangibles affect the price of a home.

Once the data base is built and population of the data base is a reality, there are many ways to use the data to get a feel for the marketplace.

The first technique is to subdivide the data into smaller subclasses. For example, there might be a class of homes that are 20 years old and are between 1200 sq ft and 2500 sq ft. Or another class of home might be homes that are from 10 to 20 years old and are bigger that 2500 sq ft. Once the homes are subdivided by basic characteristics they can be meaningfully grouped together and compared.

After the homes have been subdivided, sales trends over time can be discerned. It is usually not meaningful to look at sales trends on a daily or even weekly basis. But looking at sales trends on a quarter by quarter basis (or even on a year by year basis) makes sense.

Furthermore, with a data base you can look at trends over LOTS of homes and across lots of geographical locations. By looking at a data base that contains lots of sales that are subdivided correctly, the analyst can start to see the forest AND the trees.

The knowledge of market trends is very useful to the large investor (and to a wide variety of other people, such as the realtor). The more factual, unbiased knowledge the investor has, the more intelligent decisions that can be made by the investor.

Forest Rim Technology is a Bill Inmon company located in Denver, Colorado. Forest Rim has the technology needed to build a residential real estate data base. Bill can be reached at www.whinmon@msn.com.