Exploring the Impact of Green Labels on Residential Energy Efficiency in Europe

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 NBERGreen Building, The Economy & Public PolicyDecember 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