lOMoARcPSD|17963347
Open Boek examen Boes Ahlveldt Housing markets and
strategies, 04/11/2020
Housing systems and strategies (Technische Universiteit Eindhoven)
Studeersnel wordt niet gesponsord of ondersteund door een hogeschool of universiteit
Gedownload door Ivan Domingues (domingues.ivan100@gmail.com)
lOMoARcPSD|17963347
Berry Chicken, 7777777
Boes/ Ahlfeldt
1699 words
Exam paper Housing markets and strategies, 04/11/2020 (grade 7.2)
Header: Enter your name, S-number, # words in your paper
Please enter the answers in this document below the questions. Keep to the layout: Arial 10pt, line
spacing 1.5
Word limit: The questions count 500 words. So your paper may have 500+1200=1700 words at most.
Formulae: do not copy from article, try to write them down in a way discussed in the lectures/seminars.
You can get 50 points for this assignment.
1 Boes and Nuesch (2011) 33 points
1.1. What is the main question of the article and which externalities are valuated here?
-
“How do people valuate airplane noise and does this influence their housing preferences?”
-
This research studies the disamenities of airplane noise, which affects the attractiveness and thus the
implicit price (willingness to pay) for the associated (rental)dwellings.
1.2 Write a formula for a naïve OLS that tackles the research question. Explain the variables in the
formula as well as the subscripts. Explain why the naïve OLS coefficient might be biased.
-
Formula: Pit = α + βnoiseit + Xit + εit
where Pit is the rent for a house I in year t, α is a constant with the average price in the data, βnoiseit
estimates the effect of daytime airplane noise, Xit is a vector of covariates (housing characteristics) and εi
is an error term.
-
The problem is omitted variable bias: For example, aircraft noise can be correlated with urbanization
(airports are usually located close to cities). These effects are included in the error term and the
coefficient becomes biased.
1.3 Which method studied in the class do the authors use to answer the research question? Explain how
this method solves the problems of the naïve OLS.
-
The new flight paths were unexpected and significantly changed noise levels, making the situation
ideal for a quasi-experiment. The problem of time-varying confounders and unobserved
spatial/apartment heterogeneity is addressed by including a repeat-rent model, which measure the
effect on rental rates while controlling for unobserved apartment and neighbourhood characteristics
that remain the same.
1.4 For an event study: what is here the control and treatment group? For regression discontinuity: what
boundaries are used and how large is the boundary window? For IV: what instrument is used?
1
Gedownload door Ivan Domingues (domingues.ivan100@gmail.com)
lOMoARcPSD|17963347
Berry Chicken, 7777777
-
Boes/ Ahlfeldt
1699 words
The treatment group experienced a change in noise levels from the policy change, whereas the
control group did not. Both groups were comparable before the treatment.
1.5 Discuss the internal validity of the method used (explain the assumptions, discuss whether they hold
and how the authors tested this, if not, discuss how these could be tested).
There are 4 main assumptions with regards to event studies:
-
The treatment was random and for every group the same (the new flight paths were not based on
certain types of neighbourhoods) and people could not self-select due to the sudden policy change.
-
It is not sure whether the control and treatment group follow a common trend because it was not
explicitly stated in the article. This could have been tested by comparing historical housing prices.
-
The article does not explicitly state confounding factors. This could have included houses with
measures against airplane noise, like better insulation, to mitigate the effect that is being measured.
-
The composition of control and treatment group for the repeat-rent subsample is assumed to have
changed, because of the flexibility of rental dwellings. By contrast, households in private dwellings are
usually stuck and don’t move out as easily.
1.6 Look at Table 2. Write the formula behind the estimation in column (4). Explain all the variables you
include. How does the formula differ from the naïve OLS?
In addition to the naïve OLS, the noise effects that are measured are of dwellings after the policy measure
and for the treatment group: (Difference in difference) Ln.Pit = c + α*noise.region i + β*after.policyt + γ
(noise.regioni * after.policyt) + η*Xit + θi + Tt + εit
where Ln.Pijt is the logarithmic price value for a house, α*noise.regioni is the dummy variable for
treatment and control group (time invariant difference), β*after.policyt is the dummy variable for policy
measure (common time trend), γ (noise.regioni * after.policyt) is the average effect of the new flight
regime on apartment rents, η*Xit are neighbourhood an housing fixed effects, εit is an error term and as
an addition over the naïve OLS, θi are apartment-fixed effects and Tt is the time-fixed effect.
1.7 Interpret the main coefficient from Table 2 Column 4 (this should give answer to the main question of
the article). Is the estimated coefficient statistically significant at 1%? Use two approaches to derive this.
Coefficient is -0.035 with std.error 0.015.
Approach 1: ^β / std.error = -0.035/0.015 = 2.33 > 1.96, H0 is rejected, stat. significant on a 5% level.
Approach 2: [-0.035 – 1.96*0.015; -0.035 + 1.96*0.015] = [-0.0644;-0.0056] so significant on a 5% level.
1.8 Use the findings from Table 2 to derive monetary valuation of the policy studied in the article. Write a
formula for the monetary valuation. How could you use the outcome in a cost-benefit analysis?
-
DID: ‘valuation of noise = Mean housing prices/rents * treatment coefficient’
-
Log-linear: ‘valuation of noise = Mean housing prices/rents * noise effect per dB coefficient’
-
New price: ‘Old price – valuation of noise’
A cost-benefit analysis would compare the costs of subsidizing noise measures with the added nonmarket effects measured in the monetary valuation.
2
Gedownload door Ivan Domingues (domingues.ivan100@gmail.com)
lOMoARcPSD|17963347
Berry Chicken, 7777777
Boes/ Ahlfeldt
1699 words
1.9 Name the sensitivity checks performed. For one of them (choose which and report your choice) derive
whether there is a statistically significant difference between the estimated coefficient from the sensitivity
check and the baseline coefficient from 1.7.
-
Three sensitivity checks based on small differences in noise pollution and control region were made.
Example Table A2, column 3, test 1: The 5%-interval is [-0.0361 – 1.96*0.0155; -0.0361 +
1.96*0.0155] = [-0.0391;-0.00572] This interval overlaps with [-0.0644;-0.0056], so no significant
change between the estimated coefficient and sensitivity check.
1.10 Look at Table 3. Write the formula behind the estimation in column (4). Explain all the variables you
include. What is the difference with the formula from 1.6? What is the coefficient of interest and how do
you interpret it?
-
When the relation between noise and rental price has been established (See difference-in-difference),
the effect of one decibel noise increase can be estimated: Ln.P it = δ.noise + η*Xit + θi + Tt + εit
-
Compared to the DID formula from 1.6, the dummy variables for noise have been replaced by a
marginal effect δ.noise. The coefficient of interest is -0.0054, which is interpreted as a 0.54%
decrease in rent for every 1dB increase in noise.
2 Ahlfeldt and Wendland (2011) 17 points
2.1 What is the main research question of the article?
-
‘How does the development of rapid transport systems influence the land gradient in the city of Berlin
between 1890-1936?’
2.2 Write down a formula of a naïve OLS, explain the variables in it. Explain which reverse causality may
be present here.
-
Ln.Pit=αt+βtTravel.time.CBDit+εit
where Ln.Pit is the logarithmic landvalue for a (commercial) block i at year t, αt is the log land value in the
city centre, βt*Travel.time.CBDt is the estimated percental effect (marginal impact) on land value of travel
time from the CBD district, and εit is an error term.
-
There could be a problem with endogeneity by reverse causality: When land values in suburbs
increase for another reason, the municipality may decide to build railways to meet the new demand.
So the increase in land value explains the decrease in travel time, and not the other way around.
2.3 Write down the 2sls equations, explain the variables in them. What is the instrument? Discuss
whether it meets the two assumptions of the IV methodology.
The instrumented variable uses was a placebo that made a minimum-cost network to predict the travel
time. The first stage formula is as follows:
-
Travel.time.CBD.predictedit = γ0,t+ γ1,t popdi,t-1+ γ2,tnwdisti,t+ γ3,tdistCBDi,t+ γ4,tdistCBDi,t× nwdistit+ ωi;t
where Travel.time.CBD.predictedit estimates the travel time to the CBD for block i in year t, γ1,t popdi,t-1 is
the 10 year-lagged population density for block i, γ2,tnwdisti,t is the strainght-line distance between the
3
Gedownload door Ivan Domingues (domingues.ivan100@gmail.com)
lOMoARcPSD|17963347
Berry Chicken, 7777777
Boes/ Ahlfeldt
1699 words
centre of block i and the CBD, γ3,tdistCBDi,t is the real distance to the CBD, and γ4,tdistCBDi,t x nwdistit
is an interactive term including the distance to the CBD and network.
The second stage formula is as follows:
-
Ln.Pit=αt+βtTravel.time.CBD.predictedit+εit
Which has the same for as the original naive OLS, save for the new instrument predicted travel time.
Internal validity for instrumented variables:
-
The instrument is strong, because it is sufficiently correlated with the endogenous variable (the
instrument is statistically significant for all years).
-
The instrument is valid, because it is not correlated with the error term (the minimum-cost network is
not correlated with the actual network).
2.4 Derive from the equations which data the authors need to estimate the 2sls. Discuss whether they
have these data. Make a conclusion about data quality.
-
To estimate Travel.time.CBD.predictedit, data on population (census), the speed of trains, length of
railways and stations is needed. Population data (also needed for popdi,t-1) is available as census
data. The train data was found from historical data by (Bley, 2003).
-
To estimate distCBD, data is obtained from a GIS environment using georeferenced historic maps.
-
To estimate the Orststeile effects data, data for fixed effects is available from (Richter, 1987).
-
Land values are found using historical land values of Berlin between 1890 and 1936. The sources
use precise city maps with the lowest level being individual plots, and street indices.
The conclusion is that all data needed is obtained. The data came from detailed historic sources and the
extraction processes for GIS was appropriate.
2.5 Discuss the external validity of the results.
External validity was checked by comparing Berlin with similar researches in other cities. Most cities show
different gradients, but have comparable estimates even though is 5 times as small. The results are not
generalizable: The possible explanation of differences was that Berlin was very old and the CBD
boundaries had not changed, and Berlin was a dual capital city for both the German Reich and Prussia.
4
Gedownload door Ivan Domingues (domingues.ivan100@gmail.com)