1. Discussion of: “The Diffusion of Green Labels in the Residential Sector: Evidence from Europe” Dirk Brounen and Nils Kok “Green Residences” Dora Costa and Matt Kahn by Christopher Knittel, UC Davis and NBER Green Building, The Economy & Public Policy December 3, 2009

2. Why are these papers important?

3. Bottom line Two very nice, interesting, and important papers Both bring very rich data sets to issues surrounding the energy efficiency of residential home buildings and energy use, more generally My comments are going to be of the “I want more” variety

4. Brounen and Kok: “Green labels” Exploits the fact that since 1/08 “every” Dutch housing market transaction requires an “energy performance certificate” Score ranges from G to A+++ Post-1999 construction and monuments are exempt from mandatory disclosure, can also get a waiver Data: Transaction level data for sales with property characteristics, post- law only Analysis: Probability of having a certificate Time on the market Transaction price

5. Probability of having a certificate Logit probability model as a function of vintage, monument, housing type, quarter of transaction, property and neighborhood characteristics/fixed effects Includes entire sample Actually two separate decisions: 1. Do I get a waiver when I am “required”, 2. Do I get a certificate when I am not required Might be interesting to disentangle these Also, I’d be interested in knowing if there are “peer” effects, or evidence of “unraveling” Does what the energy efficiency of you, relative to your neighbors, matter? Certification of recent sales matter?

6. Time on market and price regressions Regress time on market and price on: Set of score dummies, Vintage dummies, TOM (if price regression) (?), Housing type dummies, new construction, Quarter of transaction dummies, Size, rooms, monument, central heat, maintenance interior/exterior, neighborhood characteristics Variation: within vintage differences in Energy Score E.g., on average how much more does a 19XXs, detached, `A’ home sell for compared to a 19XXs, detached, `G’ home May want to think of ways to account for selection

7. TOM & price results TOM results: Across all transactions, greener buildings take longer to sell Note: omitted group here is no-certificate or `G’ Would like to see the G category separated Across just certificated transaction, not the case Explanation for difference? Price results: More efficient homes sell for more Only show results for certificated sample. Why? Estimates are large: `A’ homes sell for 12% more than G homes Comment: Can we push on them more? Compare the price effects with the costs of going from G to A Does the investment pay when information is available? Can we get pre-law data and attempt to estimate benefit pre-information?

8. Concern: More time should be spent on… Is it only energy efficiency that is different? Why is one 1980s home more efficient than another? We may think it is because it was recently renovated Did the renovation only change the efficiency? Or, is most of the variation coming from differences at the time of construction? They control for central heating, whether interior and exterior that is “good”, whether insulation is “good” Is that enough? Would like to see more discussion Quality variables have wrong sign Pre-law data available? Not a perfect fix, but may be able to track the same house being sold under both regimes

9. Costa and Kahn: “Green residences” Uses a number of exciting Sacramento region household- level data sets to get at issues of: How construction vintage (i.e., codes) is associated with usage Whether the price of electricity, at the time of construction, is correlated with usage Correlations between usage and neighborhood demographics (e.g., ideologies) State-wide media conservation campaigns (“Flex your Power”) How sale prices are correlated with solar panels How much of the Rosenfeld curve can be explained by changes in demographics

10. Results Seven data sets, tons of tables! Too many to list

11. Questions Am I reading these as interesting correlations, or something more? I wasn’t sure At one point the paper calls the coefficients “treatment effects” This raises the issue just discussed Teach everyone Spanish? Ban Fox News? Can we provide additional evidence? For many of the RHS variables we can probably come up with plausible treatment effects E.g., Large Plasma TV, one small LCD Can compare these to the estimates Requires additional assumptions, but may be fairly convincing bounds PV results too large?

12. Give me more! I think the media campaign results should be their own paper More needs to be done, but this is an important result Spend an entire paper convincing the reader that nothing else was going on during these campaigns Time and Time-squared included, which is promising Almost an RD design Show the pictures! Can we see the drop in graphs? What were the costs of the campaign? Is it cost effective?

13. Nitpicking Rosenfeld effect (It’s own paper, too) Give me more! Discuss econometrics issues more Think more about what should be included and what shouldn’t For example, hybrid coefficient may grab some of liberal coefficient Functional forms We tend to migrate to lnY on lnX Does that make sense here? Do we think a plasma TV adds a certain percentage to usage? Solar panels?

14. Summary Two interesting papers using awesome data Both can push results more and do more to convince us that the estimates are causal