1. Things to do with Hedonic Prices © Allen C. Goodman, 2000

2. Things to do … 1. Estimate them 2. Interpret them 3. Make price indices

3. Value v. # of Rooms

4. Demonstration File – demo_h.xls

5. Interpret them (1) P =  0 +  1 Z 1 +  2 Z 2 +... +  t + , 1. Estimate price changes Coefficient on t term would provide “quality adjusted price.” Problems: a. Estimating it this way assumes that  terms are constant both absolutely and relatively. b.  terms may actually vary across areas.

6. Interpret them (2) P =  0 +  1 Z 1 +  2 Z 2 +... +  t + , 1. Use them for property assessment. Suppose we have a data base on all of the houses in an area. H1 = (6 rooms, 2000 sq.ft., 10000 sq. ft. lot, etc.) H2 = (5 rooms, 1000 sq.ft., 8000 sq. ft. lot, etc.) H3 = (7 rooms, 3000 sq.ft., 15000 sq. ft. lot, etc.) We estimate regression on house sales in the area and we get:

7. Problems Do current sales reflect all of the housing? Do we know enough about all of the houses to make them comparable?

8. Interpret them (3) – air pollution This is a logical succession to the discussions of hedonic price models. The arguments went something like this: If air pollution was bad, then people should be willing to pay less for property that was polluted. If this was the case, then this should give some measure of the benefits that occur from the alleviation of air pollution. It should also give you the increase in property values that might occur, if we have an alleviation of pollution.

9. Air pollution So we get: % of Clean Air Valuation 0100zi  P/  Zi Problems with this: 1. To measure benefits, you want to measure the area under a demand curve. We can see from the accom- panying diagram, that any particular hedonic price gives you only one point on the demand curve.

10. Air pollution So we get: % of Clean Air Valuation 0100zi  P/  Zi Problems with this: 1. To measure benefits, you want to measure the area under a demand curve. We can see from the accom- panying diagram, that any particular hedonic price gives you only one point on the demand curve. Estimated True

11. Air pollution % of Clean Air Valuation 0100zi  P/  Zi Problems with this: 2. As we mentioned earlier, we have a real problem with the "open city - closed city" question. If pollution abatement changes the supply of different types of land, the total land values might not change much. I haven't shown you what that all means, but I will soon. Estimated

12. Other problems … 3. We really don't know what effects pollution should really have on property values. Does twice as much pollution just look bad, is it twice as bad, or does it kill you? Ridker and Henning used census tract level data, for 1960, in St. Louis. They looked at median property value in the tract. Used a variety of explanatory variables -- for air pollution, they used sulfates. They found that a 1 unit fall in sulfates was correlated with a $245 rise in property value. This implies, they said, an increase in property values by approximately $83,000,000 (in $1960 - multiply by 5 for current dollars --- > about $415,000,000). Then use the identity: V = R/i to determine annual gain. If i = 10%, then: 83,000,000 = R/0.1  R = 8,300,000.

13. Create Price Indices P =  0 +  1 Z 1 +  2 Z 2 +... + , Example: Suppose we have reason to believe that there are two submarkets. We estimate: P 1 =  1 0 +  1 1 Z 1 +  1 2 Z 2 +... + P 2 =  2 0 +  2 1 Z 1 +  2 2 Z 2 +... + Take a bundle with (Z 1 *, Z 2 *, …) and “move” it from submarket to submarket. It is presumably always the same house. P 1 * =  1 0 +  1 1 Z 1 * +  1 2 Z 2 * +... + P 2 * =  1 0 +  1 1 Z 1 * +  1 2 Z 2 * +... + Price index = P 1 * / P 2 *

14. Create Quantity Indices P 1 * =  1 0 +  1 1 Z 1 * +  1 2 Z 2 * +... + P 2 * =  1 0 +  1 1 Z 1 * +  1 2 Z 2 * +... + Price index = P 1 * / P 2 * Q1 = P 1 /P 1 * So if a house has a price of $250,000, and the “index house” has P 1 * = 200,000. The particular house has 1.25 (= 250/200) units of “housing.”