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 PolicyDecember 3, 2009

Why are these papers important?

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

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

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?

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

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?

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

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

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

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?

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?

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?

Summary Two interesting papers using awesome

data

Both can push results more and do more to convince us that the estimates are causal