The Energy Efficiency Gap in the Rental Housing Market: It Takes Both Sides to Build a Bridge.

• Author: Lambin, Xavier

1. INTRODUCTION

   There is ample evidence that households and organizations paradoxically fail to invest in energy efficiency (EE) measures that would be profitable for them based on net present value calculations (e.g. Gerarden et al., 2015; Gillingham and Palmer, 2014; Jaffe and Stavins, 1994). Gerarden et al. (2015) identify three main reasons for this so-called EE gap: (i) market failures such as incomplete contracts, (ii) behavioral anomalies such as present bias or loss aversion, and (iii) measurement errors. (1)

   This paper focuses on the occurrence of market failure in the rental housing market. Previous studies have considered adverse selection in the rental market stemming from hidden information about relevant landlord characteristics (which we assimilate in the present paper to the energy performance of their dwellings) or characteristics available only to tenants (such as their typical energy consumption) (e.g. Giraudet, 2020).

   First, because it is difficult for tenants to observe the energy performance of the dwellings in which they live, landlords may not be able to pass on the costs of EE investments such as insulation through higher rents to the beneficiaries of these measures (i.e. the tenants). Split incentives may therefore discourage landlords from investing in EE measures for rented dwellings. Indeed, numerous empirical studies suggest that the adoption of EE measures is lower for rented dwellings than for owner-occupied dwellings (e.g. Davis, 2012; Gillingham et al., 2012; Krishnamurthy and Kristrom, 2015; Schleich et al., 2019). The availability of a signaling device, such as an energy label, may help to overcome this so-called landlord-tenant problem. In fact, the empirical literature recently surveyed by Giraudet (2020) suggests that the costs of EE measures are at least partially passed through to the tenant when energy performance certificates (EPCs) are in place.

   Second, because rental contracts may include energy expenditures, tenants with high energy consumption may choose to rent dwellings with contracts that include these costs. Indeed, the findings by Levinson and Niemann (2004) and Myers (2020) for the US are consistent with tenant self-selection into such rental contracts. For a landlord, this raises the difficult issue of how to screen potential tenants based on their expected energy consumption.

   Most of the existing literature investigates investment inefficiencies after a tenant and a landlord have matched and signed a leasing contract. In particular, the literature on split incentives often assumes that when considering investing in EE, a landlord takes her tenant's characteristics as given, but in reality this is rarely the case. The lifetime of EE investments is typically much longer than the average tenure of a tenant. Therefore, when landlords make EE investments, they do not have perfect information about current or future tenants' energy consumption. This lack of information may be detrimental to EE investments. This information asymmetry on the tenant side has not been considered in the literature so far and, as we show, is also a source of inefficiency whenever landlords pay at least part of a dwelling's energy expenditures.

   Together with individual metering, that is being rolled out in many parts of the world (Ito and Zhang, 2020) and allows consumption-based billing, EPCs help restore optimal matching and investment efficiency. Yet, several factors (costs of the meters and of metering, lack of information, behavioral biases, or landlords' marketing strategies) imply that a large share of rental contracts still include energy expenditures with a fixed payment scheme (Choi and Kim 2012). As we later show, these contracts do not guarantee optimal matching and investment efficiency. The issue is far from negligible, with 10% to 30% of rental contracts in developed economies still including at least some portion of the associated energy expenditures (Jessoe et al., 2020; US EIA, 2015; Ray et al., 2019; Santin et al., 2009). In the US, 60% of rental contracts include at least one source of energy expenses (Choi and Kim, 2012). The goal of this paper is to formalize this persistent inefficiency by recognizing that the rental housing market can be conceptualized as a matching problem with two-sided unobservables. This approach to modelling the split-incentive problem is novel and provides policy insights that have not been discussed so far.

   Existing measures such as EPCs (certification on the landlords' side) may solve the problem of information asymmetry regarding landlord characteristics. We find, however, that these measures fail to restore optimality in the selection of tenants whenever landlords pay at least some of the energy expenditures and need to find adequate tenants--i.e. when the relevant information asymmetry lies on the tenants' side. Several empirical studies (Longhi, 2015; Rehdanz, 2007) report substantial heterogeneity in residential heating expenditures, even after controlling for ownership of a dwelling. This heterogeneity correlates with tenant-side characteristics such as household size, age, employment status and income of household members, and tenant-specific behavioral variables (Best et al., 2021). The present paper shows that the EE gap is attributable not only to the lack of signaling regarding landlord-side information but also to the lack of signaling regarding tenant-side information as a source of the EE gap. This latter case has been overlooked in the literature and does not have a regulatory answer as of yet. This paper sets itself the goal of remedying that deficiency.

   This insight is, to the best of our knowledge, novel. Levinson and Niemann (2004) and Myers (2020) do find evidence that tenants self-select into certain types of contracts (whether energy expenditures are included in rent or not), but their empirical strategies do not allow for self-selection into certain levels of EE. This is our focus here, together with examining the impact of this selection process on landlords' incentives to invest in EE. Burfurd et al. (2012) show in a lab experiment with students that the relationship between landlord costs and tenants' energy use strongly affects landlords' propensity to invest in EE as well as the efficiency of such investments. Our theoretical model confirms these findings. While Burfurd et al. (2012) focus on landlord-side information disclosure as a tool for promoting efficient investment, we show that tenant-side information is also needed. As we noted earlier, the inefficiency we uncover applies to a significant share of the rental housing market.

   Recent studies acknowledge that frictions in the rental housing market may have a significant impact on energy-related choices. Fabra and Bian (2020) explicitly model the search process to study incentives to disclose EPCs. Their model assumes, though, that tenants are homogeneous, investment in EE is exogenous, and there is no pairwise matching. Our paper is the first to model the EE gap issue formally as a matching problem. This makes it possible to bring to light and study in a common setting some of the main informational issues that apply in both directions of the tenant-landlord relationship. Data from an original survey provide some empirical support for the relevance of disclosing information about tenants.

   The remainder of the paper is organized as follows. In Section 2 we propose a simple model of matching in the rental housing market and derive some formal propositions. In Section 3 we describe and discuss our empirical results. Section 4 concludes.

2. A SIMPLE MODEL OF MATCHING IN THE RENTAL HOUSING MARKET

   2.1 Base model

   Our theoretical model is inspired by the model proposed by Mailath, Postlewaite, and Sam-uelson (2017). The rental housing market is characterized by a unit mass of landlords and a unit mass of tenants. Landlords pay a share [theta] of their tenants' energy expenditures, (2) while tenants pay the remaining 1 - [theta]. In practice, it is often the case that [theta] = 0 (tenants pay their energy expenditures) or [theta] = 1. In the latter case, energy expenditures are paid by landlords, who may charge a fixed fee to tenants that is independent of actual energy consumption (e.g. a rental contract that includes energy expenditures). This happens for instance when there is no individual metering. We allow for intermediate [theta] to account for situations where landlords pay only for certain energy expenditures (e.g. a landlord does not pay for electricity but may provide central heating, or pays for heating in common areas only).

   Landlords are indexed by l [member of] [0,1], which is a measure of their idiosyncratic cost of investment in EE. The higher l is, the cheaper it is for landlords to invest in EE. Prior to being matched with a tenant, they may invest in an EE level of r [member of] [0,1], where r = 0 corresponds to no EE investment, and r = 1 corresponds to a zero-energy building. Following Mailath et al. (2017), an EE level of r is assumed to come at cost

   c (r, l) = [r.sup.2+k]/(2+k) [l.sup.k] (1)

   We constrain landlords of type l = 0 to choose r = 0 (the costs of providing EE are infinite). k is an exogenous, strictly positive parameter. As k increases away from 0, landlords become more heterogeneous with respect to the cost they pay to invest in EE. Because we assume that landlords invest in EE only once, the investment choice r coincides with the level of EE. Hence r will be interchangeably referred to as a landlord's "investment in EE" and the "EE level" of a given dwelling.

   Tenants are indexed by t e [0,1], which represents their typical consumption. For simplicity, we normalize energy prices to 1. A tenant t matched with a landlord with EE level r pays energy expenditures of (1 - [theta])(1 - r)t. The tenant's matched landlord pays the remaining (1 - r)t. Hence, the investment in EE level r generates a joint benefit of rt...