Samy Zitouni
email: samy.zitouni@psemail.eu

On the capitalization of energy labels on the French housing market

Keywords: Energy renovation, Energy efficiency, Household behavior

The building sector in France accounts for nearly a third of national emissions and energy consumption, necessitating energy retrofits and renovations to align with the government's emission reduction targets. Energy Performance Certificates (EPCs), play crucial roles in guiding this transformation. This paper explores the market capitalization of home energy labels in mainland France. Including spatial and dynamism heterogeneity and using public data on energy efficiency and property transfers, the results show that top ranked dwellings have an average 14% significant premium compared to middle rank ones. The bottom ranked houses are priced 6% lower than middle rank ones. In the case of apartments however, the results for bottom ranks are mitigated and un-significant in most cases. The premium seems to be higher in magnitude in bigger and more dynamic cities. Overall, the results may suggest that more specific data at a thin granularity may be required in order to increase the quality of prediction, especially in capturing heterogeneity.

Introduction

In France, the building sector is one of the most energy intensive, emitting more than 123 million tons of CO2 each year, which account for 27% of the country's national emissions, and 44% of its energy consumption1 . Most of this energy is due to heating, cooling and lighting equipment. Energy retrofits and renovation of households have emerged as a necessary solution to reduce building's consumption and meet with the French government's objective of dividing emissions by 4 by 2050.

Standards in terms of energy efficiency have tightened, at the European level, mainly following the Energy Efficiency Directive (EED) that requires EU countries to collectively engage to a reduction of 13% of energy consumption by 2030, with 2020 energy consumption as a baseline. In France, energy labels for residential buildings inherited from the EU Energy Performance of Buildings Directive (EPBD), are part of this policy, under the form of Energy Performance Certificates2 . They are mandatory since 2006 3 for every transaction having place on the housing market as well as, most recently, on the rental market. The French EPC has evolved over the years, and now includes a new computation method uncorrelated with households' behavior as well as an estimation of CO2 emissions. It offers owners, buyers, landlords and tenants a reliable reference scale on which to situate their housing. From January 2023, EPC ranks are used to excludes leaky homes (rank "G" dwellings that consume more than 420 kWh/m 2 •year from the rental market, a measure intended to encourage homeowners to energy, but that really concern landlords more than owners that occupy their own housing. French government's policy is planned to tighten even more, with progressively increasing restriction to an objective of no rank "E" dwellings on the rental market by 2034.

Although the utility of EPCs is not to be discussed, they raise the question of energy retrofits and renovation in France. France is indeed far from meeting its own settled objective of 1% of buildings being labeled highly energy efficient4 , only being able to label 0.2% of dwellings, according to the High Council of Climate (HCC). Near 20% of the housing stock are F and G housings (the two worst ranks), against a small 10% for energy efficient A and B buildings.

Energy retrofit investments efficiency is discussed in [START_REF] Allcott | Is there an energy efficiency gap[END_REF], [START_REF] Allcott | Social norms and energy conservation[END_REF], [START_REF] Fowlie | Do Energy Efficiency Investments Deliver? Evidence from the Weatherization Assistance Program*[END_REF], and may not be high enough for households to be incited to renovate their housing. In France, several studies conducted by ADEME (2021) find evidence that energy retrofits appear in the case of a full dwelling renovation, that may imply high costs (tens of thousands of euros). With the average duration of ownership in France being around 10 years in 2017, according to notarial institutes, the repayment duration of renovation might be too long for homeowners to enter the profitability period of their investment [START_REF] Walls | Is energy efficiency capitalized into home prices? evidence from three u.s. cities[END_REF]). This phenomenon, although it suffers from a lack of serious studies, is often cited as an explanation for under-investment in energy efficiency. Recent studies, though they do not question it, tend to highlight the magnitude of underlying costs of energy retrofit investment, most of them being non financial, as opportunity cost, disturbance, or lack of information on actual potential retrofit for a full renovation (in the case of France for instance) but they are still to be precisely evaluated.

The goal of this paper is to analyze whether or not there is a price premium for green labeled housings. Price here refers to Willingness To Pay (WTP) on the sales market, as in [START_REF] Brounen | On the economics of energy labels in the housing market[END_REF], [START_REF] Fuerst | Does energy efficiency matter to home-buyers? an investigation of epc ratings and transaction prices in england[END_REF]. In a desire to analyze households' behavior in terms of energy savings and emissions reductions, we use the mandatory CO2 emissions etiquette on French's EPC ranks to evaluate the comparative impacts between green labels for emissions and for consumption, as both scales might be far for each other: an electricity-heated housing consumes more, but is greener in terms of GHG emissions. We quantify this premium to analyze whether or not it is able to compensate the potential investment to be engaged by households to reach a green rank on the EPC scale. We refine our evaluation with each rank of the scale, and explore the problem of leaky homes.

Literature

There is a broad literature on consumer's interest for energy labels, mainly experimental, in which authors strive to show the interest people have in green labels. Nudges and social norms have emerged as a way to influence consumers, in various domains, and especially in their energy consumption [START_REF] Allcott | Social norms and energy conservation[END_REF]). Usually, policies consist in sending a consumption report that subtly compares them to their neighbors or their past consumption, following the idea they will "feel obliged to do better than the average". [START_REF] Aroonruengsawat | The impact of state level building codes on residential electricity consumption[END_REF] and Holian (2020) find a substantial decline in the residential energy consumption for buildings built after the implementation of a building codes policy. Most of the time, incentives seems to be playing with the reduction of misinformation about operating costs, transaction costs, and financial benefits of a given policy or behavior, as in [START_REF] Fowlie | Are the non-monetary costs of energy efficiency investments large? understanding low take-up of a free energy efficiency program[END_REF], who find evidence that encouraging households to follow a program, and more extensively guiding them in application processes helped improving subscription to the Weatherization Assistance Program.

On the housing market, these informative policies are likely to impact price levels. Hedonic approaches usually find a positive correlation between the "green housing" characterization and the price of the housing. [START_REF] Brounen | On the economics of energy labels in the housing market[END_REF] is one of the first study to analyze green labels effect and finding a price premium of around 3.6 % relative to non-green labeled housings, the difference being explained by underlying energy consumption of the dwelling. Other studies in different housing markets are providing consistent results : de [START_REF] De Ayala | The price of energy efficiency in the spanish housing market[END_REF] finds a price premium between 5.4 % and 9.8 % for green housing on the Spanish market, all other characteristics equal, with estimated efficiency labels based on surveys. Of course, a variety of variables induce heterogeneity of the results: [START_REF] Barreca | Epc labels and building features: Spatial implications over housing prices[END_REF] emphasize on potential spatial effects over the impact of energy attributes, while [START_REF] Fuerst | Does energy efficiency matter to home-buyers? an investigation of epc ratings and transaction prices in england[END_REF] works on the type of dwelling, stating the price effect is stronger on flats and terraced dwellings rather than on semi detached and detached homes, with still a positive correlation. Overall, there seems to exists different sources at different levels of heterogeneity in the influence of energy labels over housing prices. So far, no study were performed in the French context.

Implications of a positive price premium on housings when renovating could be an incentive for households to renovate, considering that they would recover their investment not only under the form of an energy expenditure decline but also of cashback, in the case they do not occupy the dwelling long enough. Of course, this does not cover the whole investment mechanism, that probably includes reaction to intangible costs [START_REF] Rudinger | Réussir le pari de la rénovation énergétique. rapport de la plateforme d'experts pour la rénovation énergétique des logements en france[END_REF], Gillingham and Palmer (2013), [START_REF] Allcott | The short-run and long-run effects of behavioral interventions: Experimental evidence from energy conservation[END_REF]).

Our article enriches the literature about green labels and prices premium, in a study of French housing market at the household level, using two open source government's datasets. Studies in France over the impact of renovation on price are little, although public policies often emphasize the importance of a good renovation policy. We refine the analysis to capture potential effects of spatial distribution on the French territory.

Context and Data

Context of EPC ranks in France

Energy Performance Certificates5 were first introduced in France in 2006, following the European Policy in terms of Greenhouse Gazes emission limitations. They were made mandatory in 2007 for every dwelling destined to be sold or rented. The certificate initially is an information on the housing energy consumption base on the mean of the three last annual energy bills, the main drawback being it included people's consumption behavior. A new computation method was introduced in 2013 (3CL method for, translated from french, computation of the conventional consumption of dwellings) with the aim of increasing diagnoses reliability, and address the potential bias induced by household consumption. This new approach exclusively takes into account intrinsic characteristics of the housing: the heating type, the walls composition, insulation performance, heat losses across different surfaces, etc. Although aspects of the basic information remain unchanged for the consumer, the EPC reports gathers other details and work suggestions with different cost-benefit ratios. An example from the French Ministry of Energy Transition is available in Appendix A.

These informative measures were recently completed with restrictive incentives, with the French government planning to progressively phase-out the most energy intensive dwellings from the market, beginning with ranks F and G in 2028. Tenants might also require their landlord thermal improvements for leaky, non-decent dwellings. This may increase the importance of EPC notation in decision processes on housing markets in the future.

Data

Transaction and EPC data

In this study, we use the French record of Property values, an open source governmental database that gathers notarial acts that were filed for every French transaction apart from 2013. It includes basic information on each mutation, as the type of property and potential associated lots, their price and intrinsic characteristics such as precise geographic localization and address (coordinates, cadastre and full textual address), surface, land surface, number of rooms, date of build, etc. More specifically, the mutation price is the effective price paid by the buyer to the seller. It is relevant to mention in the event that EPC etiquettes are a bargaining point in the process of buying a house. Then, a part of difference between the announced price and the final transaction could also be explained by the EPC etiquette. Every mutation is identified at the date × address level.

Unfortunately, the key information of EPC notation does not figure in the data.

Therefore, I use another dataset provided by the French Agency for the Environment and Energy Management (ADEME) and Etalab6 , that collects EPC diagnosis from early 2000's. In addition to the EPC rank, it contains the computation method, the estimated greenhouse gazes (GHG) emissions and its associated rank, a continuous variable for energy consumption, the precise address, coordinates, date and the reason for the diagnosis. EPC data are collected by government empowered professionals, that are the only ones being able to establish a diagnosis. The data is separated in two periods being before July 2021 and after July 2021. The data collection structure varies, as much more detailed information is available in the second phase of the database, including energy consumption with detailed information if more than one source is used, detailed heat loss by energy consumption items, and many more. Also, before July 2021, the energy consumption retained for the EPC computation gathered 3 items -general heating, hot water, cooling -whereas the new database includes 5 items: the three previous, lighting and auxiliaries. As a consequence, to compensate for the potential general rise of energy consumption, the frontiers of different ranks increased a bit, but unevenly. The details between old and new ranks can be found in Appendix B.

I match both datasets on the base of their textual address and geographic localization. Although EPC diagnosis are mandatory for housing transactions since 2007, having a housing in the EPC database does not guarantee to find it in the housing sells database. As a result, matching data drastically decreases the number of identified households. I assume that the match is correct for apartments, even though we do not have the very precise information of address (apartment number, etc.), for many reasons. First, a large majority is properly flagged as "Apartment" in the database, which allows for a late phase-out. Second, the fact that a diagnoses exists sharply increases the probability to find the corresponding transaction in the EPC database. Third, a good amount of buildings are homogeneous in terms of characteristics (central heating system, etc.) that has caused the EPC diagnosis to be centralized for buildings with less than 50 units, and usable for a transaction. If a housing is identified in both databases, I control for duplicate reports imposing a time window between two transactions at the same address and with the same file number7 . This treatment still allows for multiple transactions for each dwelling. Each transaction is associated with the latest up to date EPC diagnosis. A dwelling might have different diagnosis, mainly if the sellers had performed energy retrofit works, or if the used EPC computation method has changed. Three regions of mainland France are not represented in the transactions dataset due to legal specifications : Haut-Rhin, Bas-Rhin, and Moselle.

To account for potential reporting errors, or simply to eliminate outliers, I restrict the energy consumption values between the 0.01 and 0.99 percentiles. Beyond, not representative of the French housing stock. It allows the elimination. I am able to identify houses in every region, however it is not the case for every department, as can be seen on Figure 4 Apartments seem to have a greater turnover than houses. In the same time, several appartments could be associated with an only address because of the lack of precision in coordinates and address.

Table 2 represent descriptive statistics over the whole sample of the final merged dataset.

Spatial data

Intrinsic characteristics of dwellings should not be considered as the only ones able to explain for the transaction prices. Although a common way to take the dwelling's localization into account when explaining its price is adding space fixed effects, which contribute refining the between spatial units estimation more than the estimation within a unit. In order to counter for these effects, I implement geographic attributes that aim to enrich my explanation of the price. The idea is that obviously some localization imply a higher price than other. In the same time, there would be a possibility that dwellings' energy efficiency is not distributed randomly across mainland France: places where the housing market is dynamic or higher would not encourage energy retrofit works, as sellers would anticipate a rather low price devaluation caused by a bad score in efficiency. On the other way around places far from dynamic markets would encapsulate more green homes, being either build later in time with the expansion of urban areas, or renovated for other reasons than anticipated market competitiveness. Therefore, in average, the effect of greener EPCs would be underestimated. Thus, a challenge is to find a measure of spatial distribution of housing dynamics that is consistent across mainland France.

To given by Table 3 This measure has been introduced in 2020 by INSEE in replacement of 2010 and before urban areas. 4 Set up

Dumping at EPC frontiers

Although the design of EPC ranks make it seem to be suitable for a regression discontinuity design, there is a concern about whether the data verifies the main assumptions, so that each treatment, being obtaining a letter compared to another, could be considered as as good as random around the different cutoffs, which correspond in our case to letters jumps. On Figure 1, it is clear that dwellings are distributed more frequently in the lower tail of each EPC rank.

One of the main important necessary assumption is that individuals, that is to say homeowners in our case, are unable to manipulate the treatment. In the case of EPC ranks design, there is a possibility that the sharp thresholds design can affect the verified distribution of energy efficiency.

There already have been evidence in literature that people are able to manipulate their placement on EPC ranks. It has been the case in Ireland, that has a close definition of EPCs (see [START_REF] Collins | Bunching of residential building energy performance certificates at threshold values[END_REF]). They make a polynomial estimation of the counterfactual densities absent Nearly all test statistics are significant at the 1% level. The most steep drops are for D-E, E-F and F-G jumps. However, we are not able yet to attribute these high discontinuities to the price premium found in the literature. One of the main reasons is that those sharp drops occur for ranks that are not green ranks, but are the most populated. It is plausible that rather than a price increase, people would only want to stay in the average consumption letters, more to deter someone not buying the house rather than attract someone buying it.

Mechanisms behind the manipulation of EPC ranks are unclear. Although it could be the result of EPC assessors empathizing with homeowners and adjusting a bit their rating to rank them in a superior class, [START_REF] Collins | Bunching of residential building energy performance certificates at threshold values[END_REF] finds no evidence that any assessor gives specifically better grades in Ireland. Also, he finds little correlation between a competitive environment for assessors and better EPC grades. However, although it is impossible to detect, there might be situations in which assessors would have an interest in giving better ranks. For example, a highly competitive market where goods are administrated by agencies could be a source of stable revenue for them if the agency and clients are satisfied with the assessor. Another idea is that it could be easy for homeowners to lie or manipulate some variables used in the computation of the EPC, whether it is voluntary or not. For example, the heating system play a high role, and two similar dwellings could have very different ratings, which may be independent of people's will.

Bunching implies that regression discontinuity design is not the appropriate specification to guarantee identification of the EPC effect on housing price. We stay with the classical hedonic approach as in most of the papers relative to the subject [START_REF] Brounen | On the economics of energy labels in the housing market[END_REF]; de Ayala et al. ( 2016); [START_REF] Barreca | Epc labels and building features: Spatial implications over housing prices[END_REF]; [START_REF] Evangelista | On the use of hedonic regression models to measure the effect of energy efficiency on residential property transaction prices: Evidence for portugal and selected data issues[END_REF]). We use French cities' attraction data to control for omitted variables on non-intrinsic factors that could affect the price.

Hedonic specification

Running a model that would encapsulate all the database may prove complicated. Indeed, the criteria for the attribution of given EPCs are unevenly distributed in time, before July 2021 and after July 2021. The transformation of threshold is, in addition, non linear, meaning that some may have their threshold conserved, some others may not. In a matter of consistency, I run each model on two separated datasets. I also distinguish between houses and apartments, trying to avoid the importance of a land associated with a transaction. On each model that I run, I than have a set of 4 vectors of coefficients that express EPC's impact on the housing price.

We adopt the following general specification:

log P i = β 0 + β 1 EPC i + β 2 C i + λX i + µS i + γ i + ζ t + ϵ i (1)
Where log P i is the logarithmic transformation of the housing price, and EPC i and C i are respectively the EPC label and the continuous variable that estimates houses' energy consumption. Vector X i is a set of control variables which are intrinsic characteristics of the dwellings (Surface, surface of the associated land, number of main rooms, age and type of residential structure, etc.). Vector S i encapsulate several characteristics relative to the environment of the dwelling, mainly inherited from the attraction zones dataset (the type of city it is situated in relative to the nearest attraction pole, the metric distance to the nearest attraction pole, the type of attraction, the urban or rural characteristic, etc.). Vectors γ i and ζ t account respectively for postcode and time fixed effects. I further test for the metric distance from the house i to the city center in the vector S i (measured by city halls), trying to better explain variance of the price within any fixed effects. The point of the attraction zones approach is to have a good and consistent measure of dynamism and attractiveness for the whole country.

Main results

First specification

The results of the first specification are presented in Table 4, with the data limited to Houses for the first column and apartments for the second column. In the following, every percentage value given is obtained by exp(β) -1, β being the studied coefficient in the regression table 4. Several characteristics of dwellings have a positive and significant impact on the price. For both houses and apartments, the more recent, the higher the price. However, there seems to be a "vintage premium", with old houses and apartments having a positive and significant impact, higher for dwellings built in between old and new times. Also, an additional room increases the price by 22%

for an apartment and 10% for a house, the main reason being that the average house has two times more rooms (see Table 2). The same way, we find that an additional square meter increases the price by 1.2% for a house and 2.1% for an apartment, with similar reasons than for the number of main rooms. Then, the distance to the city center, that is in reality approximated by the distance between a given dwelling and the city-hall associated with the municipality, has a negative impact on the price for houses, and positive and significant for apartments. My guess on the sign is that apartments near the city center might be smaller and as a consequence lower in price. Further, we will study heterogeneity in the results limiting the database to cities of different classes.

I express EPC price premium relative to class D, which is the most populated class. Note that I dropped rank A from the database as ts under-representation biased down results. Gathering ranks A and B also includes this bias. The fixed effects approach allow for a relatively good R 2 at the general level. However, I obtain a pretty low value for the within R 2 , meaning that I am unable to explain a lot of the variance for a given postcode. Having a good explanation power in this situation is important because it acts as the direct reflect of ability of the data to correctly explain the price of dwellings. I find a positive and significant impact for better EPcs than the average (ranks B and C). For example, a rank B dwelling is valued 8% more than a rank D dwelling. For ranks B and C, premiums are pretty consistent between houses and apartments. In the case of low-efficiency buildings, we find that coefficients for houses are strictly negative and all significant. A rank G house is 6% less expensive than an equivalent rank D house. There is a large jump in the effect that nearly doubles between rank E and F, but the difference between ranks F and G does not stand out. Interestingly, the devaluation is lower than the premium in magnitude, which could suggest that low-efficiency houses main still have market value due to potential other factors. In the case of apartments, the devaluation smaller in magnitude, but negative and significant for ranks E and F, but is not found statistically different from 0 for rank G. Also, the devaluation is way smaller than for houses. This could come from the fact that a good majority of apartments may benefit from collective heating, causing people to have a fixed amount of charges to pay every month, no matter what. Buyers and in-occupants may then have their awareness of energy efficiency lowered, as collective charges vary a lot between co-ownerships, whether it is monitored by an agency or not, a syndicate, etc. Bargaining power is also very low for owners in terms of general life of the building. Added to the fact that collective EPC reports (for the building) could before July 2021 be used solely in the sake of selling, the invisible proportion of the charges dedicated to energy may overcome the visual effect of EPCs. Also, some reasoning could be done in terms of general inertia of the building, in the hope of benefiting from neighbors heating. Eventually, apartments could be situated more frequently in tensed markets, which in addition to all other arguments, may explain why care for good efficiency may not be valued anymore when buying.

The estimation of energy consumption also has its importance. In Appendix F are the same table in the case I do not include this continuous measure of estimated energy consumption of the dwelling.

This variable depends on a lot of factors in the case of EPC report establishment. Some of them may depend on anterior bills, some of them may be more liable estimations. As consumption is located at the bottom of ranks for the most, it might capture in-rank variations. It is still coherent with the notion of "visual effect", as estimated primary energy consumption and estimated bills are available on the EPC report.

6 Results' consistency

Influence of market dynamics

A concern that may arise with the potential influence of market dynamics that may overcome the potential influence of EPC ranks on the housing price. Housing market can be influenced locally by many factors. Although we cannot take all of them into account, I argue that my analysis may have to be refined depending on those local housing markets, that I define following the city catchment area zones. In the span of the five types of attraction, that account for five different structures and intensities. I choose to run a fixed effects regression on each of them, with a separation between houses and apartments once again. All regression tables will be available below, but I choose to only represent effects of EPC ranks on the price. Effects are expressed in percentages, with the baseline rank still being rank D. Other variables relative to the zone I am looking at are expressed, that mainly ranks them into sizes. Attraction zones can be detailed by poles and peripheral zones of different sizes. Size categories are provided by INSEE, by inhabitants: less than 50,000, between 50,000 and 200,000, between 200,000 and 700,000, more than 700,000.

I present in Table 5 the coefficients corresponding to the variable EP C i in the first specification

(1). For the sake of simplicity and place, I only put show these coefficients, there magnitude might increase or decrease in favor of others. The effect of EPC ranks with respect to different scales of dynamism is relatively mitigated. First, it is noticeable that the different effects are more frequently significant for houses than for apartments. Of course, the number of available observations highly depends on the geographical context and organization. It could suggest that apartments are in average more sensitive to market dynamics than houses, potentially due to the highest turnover on the market. Then, other factors could be more influential on the price, such as the precise localization, that we are not able to take into account in this study. In both cases, houses and apartments, the "green effect" (that is the effect of having a top rank dwelling relative to mid rank) seems to be higher in magnitude in smaller poles and peripheries than for bigger ones, suggesting that value attributed to EPC is higher in non dynamic markets.

In another approach, in Table 6, I gather the most probable dynamic housing markets in France, and compare it to the initial analysis of Table 4. To do so, I restrict the database to cities which main pole is among Paris, Lyon, Marseille, Nantes, Strasbourg, Lille and Bordeaux. For example, cities which main pole is Lyon are all cities attached to Lyon, that is to say Lyon itself, the suburbs, and other cities near Lyon that send 15% at least of workers every day. To try to capture heterogeneity, I still control for the distance to city center. I find that the green premium for houses seems to decrease relative to the aggregated version on France for ranks B and C. Although rank below D are not significant, their magnitude is also lower. This is consistent with the previous results by pole and periphery sizes. In the case of apartments, the results quite differ: not only the premium for ranks B and C is higher, but the devaluation is also greater and significant for ranks E and F.

It is surprising considering that it could be anticipated that the premium and devaluation would be lower in percentage in dynamic markets, where the average per meters squared price is higher in average. Although I struggle finding significance for some labels and having a high within R 2 , it may signify that on average buyers tend to value EPC ranks in major cities, maybe as they can be a discriminant negotiation point in tight markets. Of course, I do not exclude the fact that the effect could be biased towards larger magnitudes due to unobserved attributes. It is hard to infer that the difference would come from buyers' behaviors and beliefs, that would be heterogeneous between major cities and other ones, because I cannot find the same effects on houses.

Time consistency

Looking at the evolution of trends in price of my dataset provides the same insights as what is well known in France for the period 2014 -2021, which is an increase of average price (see 5 in Appendix G). I look at the coefficient estimates of a postcode-fixed-effects regression very similar to the first specification to see whether different growing rates in different EPC ranks can be attributed to the EPC.

Yearly effects are mostly consistent in the case of green premiums (that is to say the coefficient attributed to rank B of the EPC), and the effect stays pretty consistent over the observed period, for both houses and apartments. In the case of devaluation, which is the coefficient attributed to rank G of the EPC, it is unclear how it behaves. In the case of houses, it is negative and significantly different from zero most of the time, and positive and insignificantly different from zero which decreases the credibility of the coefficient at this particular date. Surprisingly however, the devaluation coefficient is positive at several dates in the case of apartments. This potentially suggest that, once again, apartments are way more sensible to market dynamics, or simply other price drivers than the EPC. Additionally, there is a probable direct relationship with the rental market way stronger than in the case of homes, due to the most frequent localization near city centers or near suburbs, where households may either be more tempted to invest for the pure sake of renting, or either have inherited. As a consequence, low efficiency apartments may suffer less from potential devaluation attributed to efficiency rating, because of their number on the market, and a tense market. This could encourage future research to focus on effects on well defined shocks on the rental market, to see how it impacts the price of housing. For example, one could imagine that the announcement of the climate and resilience act9 , that will further ban the rental of F and G dwellings, had an impact and will have an impact on sells approaching the deadline. Owners may want to sell to get cash rather than engaging costly renovation work to get to a superior rating, that may eventually be obsolete in several years. In the same time, buyers and renters may be aware of such interdiction and use the EPC rank as a bargaining tool. It is then likely that the devaluation becomes higher both by bargaining and massive supply of dwellings.

Although I mainly perform my analysis over a set of transactions occurring before 2021, in Appendix H, I show the results for the first specification (1) in the case of after July 2021 transactions.

As said in 4.2, the thresholds for the attribution of the EPC are not the same between the two database, which is obviously a problem for having a good estimation strategy. Still, I find that devaluation becomes negative and significantly different from 0 for both houses and simply negative for apartments from July 2021. Once again, this conveys the idea that I may fail to capture hidden mechanisms on the housing market specifically looking at efficiency ratings.

Conclusion

Efficiency ratings are particularly important in the context of reducing global emissions, for many reasons. If measured correctly, their capitalization on the housing market captures buyers willingness to pay for energy efficiency. For sellers and more generally homeowners, it also reflects how can an energy renovation still be (or not be) beneficial for their wallet. As the average occupying duration is around 10 years in France, people that invest in a full and efficient energy renovation might not have the time to recover from their investment. Selling at a higher price is obviously the best way to ensure the profitability of their investment (if they care for it of course).

In this paper, I try to estimate the capitalization of EPC rank on the French housing market, using

  in Appendix C. From the [INSERER OBSERVATIONS] observations in the transactions dataset and the [INSERER OBSERVATIONS],we obtain a 830,413 unique observations database, that are distributed as in 1

  do so, I use the French National Institute of Statistics and Economic Studies (INSEE) city catchment area zoning 8 . The zoning defines attraction poles that are built on population density and number of employments. A pole can correspond to one or several municipalities. Then, surrounding localities that send at least 15% of their active population are considered to be part of the ring municipalities, that are attracted by the pole. The zoning ranks different poles as a function of their attraction power and their population density. From my database point of view, it means that every city (and further every house) can be defined as part of an attraction pole. Attraction poles cannot overlap, and when two or more poles are concerned, the most dominant one is always preferred. As a consequence, I am able to cut the French territory into attraction areas. The distribution of observations and unique transactions across different types of catchment zone is

Figure 1 :

 1 Figure 1: Threshold effects in EPC distribution

Figure 2 :

 2 Figure 2: Density estimates of McCrary's test for each cutoff

Figure 3 :

 3 Figure 3: Time consistency

  national data and a standard hedonic estimation strategy. Although I refine the result by a measure of local dynamism to account for market dynamics, I find both intuitive and counter intuitive results on how different labels explain the price of a given dwelling. More specifically, if the green premium is always positive and significantly different from 0, the devaluation caused by being part of the G rank is still unclear. Of course, I might lack of explaining power in my different regressions, because Upper consumption frontiers for each EPC rank C Identification of dwellings (a) Identification at the region level (b) Identification at the department level

Figure 4 :

 4 Figure 4: Identified houses inherited from the matching of the two databases Note: The "Size" scale corresponds to the number of identified houses. Departments that are not identified are Haut-Rhin, Bas-Rhin and Moselle, as the three regions being a part of a legal specificity. This implies sells data are not accessible.

Table 1 :

 1 Distribution of unique addresses and transactions by dwelling type

		Apartments Houses
	Unique addresses	105,669	350,466
	Unique transactions	436,472	393,941

Table 2 :

 2 Descriptive statistics over the final merged table

		Apartments Houses	Total
		436,472	393,941	830,413
	Transaction value	178,385	228,560 202,188
		(137,901)	(143,479) (142,790)
	Gross floor area	56.8	102.8	78.4
		(48.9)	(146.5)	(108.9)
	Missing	3,032	10,698	13,730
	Number of main rooms	2.0	3.9	2.9
		(1.5)	(1.7)	(1.9
	Energy consumption	227.5	238.9	232.8
		(91.1)	(101.8)	(96.5)

Note:

Values represent means over the corresponding type of housing. Between brackets are the associated standard deviations.

Table 3 :

 3 Number of observations and unique transaction of by catchment zone type

	Type of municipality Unique transactions
	Central	326,404
	Part of a main pole	228,165
	Part of a secondary pole	6,452
	Peripheral	239,144
	Out of attraction areas	30,248
	Total	830,413

Note: Additional explanations about what precisely are types of municipalities can be found in Appendix D It is based on the national census of 2016 and the total number of inhabitants of each municipality of 2017. Although the criteria does not change, there are a proportion of municipalities that can be merged, especially between 2014 and 2019 according to INSEE. Their method is to allocate to a merged municipality in year n the category of year n to both initial municipalities for year n -1. Corrected databases are all provided by INSEE. Although municipalities could change, the zones in themselves stay the ones that are defined in 2020. Thus, I have to assume that this definition is accepted for all the years I have available in my database, meaning 2013 to 2023. No other database provides thinner evolution in time. In the end, I attribute each observation an attraction zone depending on its municipality, using each year's attraction database to avoid losing municipality names.

Table 4 :

 4 Results for the first specification, data before July 2021

	Dependent variable:		ln P
		(1)	(2)
		Houses	Apartments
	EPC
	B	0.0837 * * *	0.0873 * * *
		(0.0081)	(0.0135)
	C	0.0488 * * *	0.0466 * * *
		(0.0042)	(0.0072)
	D	-	-
	E	-0.0333 * * *	-0.0162 * *
		(0.0040)	(0.0063)
	F	-0.0603 * * *	-0.0292 * *
		(0.0077)	(0.0123)
	G	-0.0638 * * *	-0.0003
		(0.0132)	(0.0211)
	Estimated energy consumption	-0.0006 * * *	-0.0003 * * *
		(4.59 × 10 -5 )	(6.53 × 10 -5 )
	Gross surface	0.0012 * * *	0.0021 * * *
		(0.0004)	(0.0007)
	# Main rooms	0.1095 * * *	0.2028 * * *
		(0.0059)	(0.0128)
	Distance to center	-3.43 × 10 -6	0.0006 * * *
		(6.25 × 10 -6 )	(2.68 × 10 -5 )
	Date of built
	Before 1900	0.2023 * *	0.1224 * * *
		(0.0795)	(0.0223)
	1900-1950	0.1810 * *	-0.0132
		(0.0801)	(0.0162)
	1950-1970	0.1915 * *	-0.1014 * * *
		(0.0802)	(0.0171)
	1970-1990	0.2340 * * *	-0.0130
		(0.0803)	(0.0154)
	1990-2000	0.2996 * * *	0.0872 * * *
		(0.0804)	(0.0176)
	2000-2010	0.3149 * * *	0.1660 * * *
		(0.0805)	(0.0175)
	2010-2021	0.3484 * * *	0.1840 * * *
		(0.0811)	(0.0205)
	Property age	-3.22 × 10 -5 -1.58 × 10 -5 * * *
		(2.59 × 10 -5 )	(5.35 × 10 -6 )
	Fixed-effects
	Postcode	Yes	Yes
	Transaction year	Yes	Yes
	Fit statistics
	Observations	241,162	291,093
	R 2	0.63066	0.48489
	Within R 2	0.29445	0.29045

Clustered (Postcode) standard-errors in parentheses

Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Table 5 :

 5 Effects on price on different housing markets

(a) Effect of EPC ranks on housing price, for houses Less

than 50,000 Between 50,000 and 200,000 Between 200,000 and 700,000 More than 700,000

		Pole	Periphery	Pole	Periphery	Pole	Periphery	Pole	Periphery
	B	0.102***	0.0234	0.1402***	0.1353***	0.0509*	0.0922***	0.0689*** 0.0617***
	C	0.064*** 0.0456*** 0.0801***	0.0806***	0.0499***	0.0384***	0.0332*** 0.0317***
	E	-0.0461*** -0.0423** -0.0508***	-0.0653***	-0.0386***	-0.04***	-0.0054 -0.0183**
	F	-0.0741** -0.087*** -0.0921***	-0.1206***	-0.073***	-0.0722***	-0.0056	-0.0212
	G	-0.0381	-0.014	-0.1075***	-0.1572***	-0.0462	-0.0976***	-0.0208	-0.0506*
	N obs	9,824	1,435	27,553	3,974	67,850	9,652	156,242	14,563
			(b) Effect of EPC ranks on housing price for apartments

Less than 50,000 Between 50,000 and 200,000 Between 200,000 and 700,000 More than 700,000

		Pole Periphery	Pole	Periphery	Pole	Periphery	Pole	Periphery
	B	0.0737 0.2578* 0.1174*	0.0928	0.0655**	0.0731	0.0898*** -0.0324
	C	0.0441	0.0858 0.0467**	0.0474	0.0277*	0.0369	0.0537*** -0.0016
	E	-0.0176 0.0115	9e-04	-0.0502	-0.0105	-0.0277	-0.02**	4e-04
	F	0.0253 -0.2061	-0.0376	0.0128	-0.0364	-0.0264	-0.036**	0.0556
	G	0.0194 -0.1592	0.0117	0.067	0.0305	0.0488	-0.0195	0.0258
	N obs 15,467	11,240	25,088	30,543	29,009	36,752	52,127	40,936

Table 6 :

 6 Regression table for model (1) and most dynamic markets

	Dependent variable:	ln P
		(1)	(2)
		Houses	Apartments
	EPC
	B	0.0561 * * *	0.0977 * * *
		(0.0134)	(0.0202)
	C	0.0278 * * *	0.0539 * * *
		(0.0064)	(0.0118)
	E	-0.0102	-0.0226 * *
		(0.0068)	(0.0088)
	F	-0.0110	-0.0436 * *
		(0.0130)	(0.0180)
	G	-0.0328	-0.0193
		(0.0223)	(0.0319)
	Energy consumption	-0.0004 * * *	-0.0002 *
		(6.77 × 10 -5 )	(9.73 × 10 -5 )
	Gross surface	0.0028 * * *	0.0024 * * *
		(0.0004)	(0.0007)
	# Main rooms	0.0854 * * *	0.2136 * * *
		(0.0073)	(0.0160)
	Distance to center	-3.7 × 10 -5 * * *	-0.1257 * *
		(3.83 × 10 -6 )	(0.0515)
	Date of build
	Before 1900	0.3064	0.0532 * *
		(0.2221)	(0.0244)
	1900-1950	0.2935	-0.0350 *
		(0.2292)	(0.0184)
	1950-1970	0.3312	-0.1030 * * *
		(0.2306)	(0.0196)
	1970-1990	0.3408	-0.0159
		(0.2326)	(0.0161)
	1990-2000	0.3817	0.1005 * * *
		(0.2344)	(0.0202)
	2000-2010	0.3836	0.1826 * * *
		(0.2360)	(0.0181)
	2010-2021	0.3808	0.1308 * * *
		(0.2364)	(0.0238)
	Property age	0.0001	-1.2 × 10 -5 * * *
		(0.0001)	(4.1 × 10 -6 )
	Fixed-effects
	Postcode	Yes	Yes
	Transaction year	Yes	Yes
	Fit statistics
	Observations	77,221	127,741
	R 2	0.58898	0.41096
	Within R 2	0.32015	0.30163

Clustered (CP) standard-errors in parentheses

Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Table 8 :

 8 Results for the first specification, data before July 2021

	Dependent variable:		ln P
		(1)	(2)
		Houses	Apartments
	EPC
	B	0.1644 * * *	0.1193 * * *
		(0.0078)	(0.0124)
	C	0.0946 * * *	0.0651 * * *
		(0.0038)	(0.0064)
	D	-	-
	E	-0.0872 * * *	-0.0401 * * *
		(0.0030)	(0.0050)
	F	-0.1797 * * *	-0.0818 * * *
		(0.0045)	(0.0071)
	G	-0.2594 * * *	-0.0856 * * *
		(0.0068)	(0.0126)
	Gross surface	0.0002	0.0021 * * *
		(0.0001)	(0.0007)
	# Main rooms	0.1230 * * *	0.2028 * * *
		(0.0029)	(0.0127)
	Distance to center	1.08 × 10 -5	0.0006 * * *
		(6.87 × 10 -6 )	(3.15 × 10 -5 )
	Date of built
	Before 1900	0.2052 * *	0.1196 * * *
		(0.0835)	(0.0222)
	1900-1950	0.1776 * *	-0.0146
		(0.0843)	(0.0162)
	1950-1970	0.1851 * *	-0.1019 * * *
		(0.0844)	(0.0171)
	1970-1990	0.2319 * * *	-0.0142
		(0.0847)	(0.0154)
	1990-2000	0.3044 * * *	0.0872 * * *
		(0.0850)	(0.0176)
	2000-2010	0.3209 * * *	0.1654 * * *
		(0.0852)	(0.0175)
	2010-2021	0.3511 * * *	0.1833 * * *
		(0.0857)	(0.0205)
	Property age	-4.31 × 10 -5 -1.61 × 10 -5 * * *
		(2.99 × 10 -5 )	(5.35 × 10 -6 )
	Fixed-effects
	Postcode	Yes	Yes
	Transaction year	Yes	Yes
	Fit statistics
	Observations	254,823	293,527
	R 2	0.62186	0.48552
	Within R 2	0.26641	0.29005
	Clustered (Postcode) standard-errors in parentheses
	Signif. Codes:

***: 0.01, **: 0.05, *: 0.1

Table 9 :

 9 Results for the first specification, data after July 2021

	Dependent variable:	ln P
		(1)	(2)
		Houses	Apartments
	EPC
	B	0.1303 * * *	0.0286
		(0.0142)	(0.0306)
	C	0.0585 * * *	0.0248 * * *
		(0.0063)	(0.0095)
	D	-	-
	E	-0.0808 * * *	-0.0309 * * *
		(0.0060)	(0.0117)
	F	-0.1657 * * *	-0.0223
		(0.0099)	(0.0206)
	G	-0.2575 * * *	-0.0095
		(0.0156)	(0.0321)
	Energy consumption	-0.0001 *	-0.0002 *
		(5.98 × 10 -5 )	(0.0001)
	Gross surface	0.0056 * * *	0.0062 * * *
		(0.0002)	(0.0006)
	# Main rooms	0.0146 * * *	0.0737 * * *
		(0.0015)	(0.0042)
	Distance to center	0.0005	0.0668
		(0.0008)	(0.0410)
	Date of Built
	Before 1900	-0.2419 * * *	-0.0149
		(0.0325)	(0.0445)
	1900-1950	-0.2054 * * *	-0.0600 * *
		(0.0226)	(0.0283)
	1950-1970	-0.0782 * * *	-0.1481 * * *
		(0.0194)	(0.0284)
	1970-1990	-0.0284	-0.1456 * * *
		(0.0173)	(0.0251)
	1990-2000	-0.0174	-0.1024 * * *
		(0.0179)	(0.0237)
	2000-2010	-0.0143	-0.0498 * *
		(0.0171)	(0.0198)
	Property age	0.0002	0.0003
		(0.0002)	(0.0003)
	Fixed-effects
	Postcode	Yes	Yes
	Transaction year	Yes	Yes
	Fit statistics
	Observations	71,748	76,994
	R 2	0.66858	0.31193
	Within R 2	0.40084	0.12621
	Clustered (Postcode) standard-errors in parentheses
	Signif. Codes:

***: 0.01, **: 0.05, *: 0.1

According to the French Ministry of Ecological Transition and Territorial Cohesion

In French, "Diagnostic de Performance Energétique"

Décret n°2006-1147 du 14 septembre 2006 relatif au diagnostic de performance énergétique et à l'état de l'installation intérieure de gaz dans certains bâtiments.

"Bâtiment Basse Consommation", BBC

In French "Diagnostique de Performance Energétique"

Department of the French Inter-ministerial Digital Directorate

Sometimes two transaction dates are separated by a few day or a month, which might correspond to market value estimation and transaction, or the agreement of price and the effective transaction date

In French "Zonage en Aires d'Attraction des Villes"

https://www.legifrance.gouv.fr/jorf/id/JORFTEXT000043956924

However, the devaluation is stronger for houses: rank G houses are in average 6% less expansive than rank D dwellings, whereas the effect is quasi non-existent for apartments. Being in a more dynamic area seem to decrease the relative effects for premium and devaluation, meaning the price is nearer the price of rank D dwelling in percentage points, especially for houses. Of course, the absolute value may stay high. In the case of apartments, results are less intuitive and less clear: being in an important city center seem to increase percentage points for green premiums, suggesting that market dynamics may overcome the effect attributed to energy efficiency. Devaluation estimates are the harder to interpret, because I often lack significance, especially for apartments. My main explanation is once again that market dynamics, that may be influenced by French policies as the climate and resilience act, are less intuitive than expected, with for instance, in the case of low efficiency apartment, a strong bond with the rental market that may require a deeper structural analysis.

A EPC diagnosis example

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