How to Sell a House in Five Easy Steps
Use good scientific methods to get results.
Collect empirical data.
- Make sure sample size is big enough to have sufficient statistical power.
Note: Empirical data is information that is acquired through observation or experimentation and is an essential part of the scientific method. Good empirical data is evidence based and must be gathered in sufficient quantities in order to conduct valid statistical analysis. Without sufficient quantities even good data can lead to inaccurate conclusions.
Example: Flip a fair coin 3 times and the chances you will get tails 100% of the time are very good (even though each flip is independent of the other). Flip that same coin 100 times and tails will always come up around 50% of the time. One house has similar features to your subject and sold for $X, could be a fluke, 50 houses have those features and sold for $X could be a correlation.
Analyze the data.
- Identify correlations between variables.
- Account for random variation.
- Check inferences against existing literature.
Note: It’s important to go beyond simply reporting observations. One must also accurately analyze the data using standard and accepted statistical methods in order to form logical and valid conclusions. Regression analysis, t-test and ANOVA have been used extensively and successfully to uncover correlations and make sound predictions and minimize the negative effects random variation can have on those predictions. It is critical to remember that statistical models do not produce “proof” but can only support a hypothesis, reject it or do neither. Once an analysis is made, research journals like that of The American Real Estate and Urban Economics Association should be checked for similar or dissimilar results.
Example: Does time on the market effect sale price? Most research suggests that it doesn’t. There is a correlation between homes with granite counters and a higher sale price, do granite counters themselves cause a house to sell for a higher price or do they usually accompany other features and benefits that directly contribute to a higher sale price?
Use inferences to build pricing and marketing plans.
Note: You have collected the data, analyzed it and made a prediction; now what? Right or wrong, guessing a future sale price is only a small skirmish in the war to sell the house. Use your price prediction and data on how long it typically takes to sell a house when the correct list price is obtained to build a pricing plan. Use findings from behavioral economics and behavioral marketing experiments to build marketing.
Example: Using the “!” in the remarks about a property have been shown to correlate negatively with price and market time, probably best not to use “!”.
Present findings and options to client.
- Make sure client understands data.
- Make sure client is comfortable with pricing and marketing plans.
Note: There is one thing and one thing only that will keep a house from selling and that is the owner. If the owner doesn’t understand or doesn’t feel confident and comfortable with your data and recommendations the house is unlikely to sell. Make sure your clients understand and are confident and comfortable!
Repeat until sold.
Note: Scientific “truths” are held conditionally. Keep collecting, testing and analyzing as conditions might have changed or new data become available after initially listing a house. Make sure the home owner knows this is an important part of the process.