Analysis of the housing finance and housing data landscape: the case of HEVC Côte d'Ivoire
It is important to advocate for the development of housing and housing finance sectors in Africa while focusing on proper tools to enable such development in every African country. To attain this goal, we believe that it is necessary to shift perceptions in the private and public sectors that often view housing as a social good, to viewing it as a financial asset and an active component of economic development at both the macro and micro level. In so doing, CAHF designed its Housing Economic Value Chain (HEVC) studies and its Housing Cost Benchmarking (HCB) to offer government and key stakeholders an understanding of the economic contribution of housing to the generation of incomes during the building process (at a household, developers and contractors level) and its potential to stimulate the local and national economy (at a housing market level).
This blog discusses the housing finance and housing data landscape in Côte d’Ivoire that arises from CAHF’s recent HEVC analysis of that country. The purpose of the Ivorian economic value chain analysis is to describe the linkages between various sectors of the Ivorian economy and to quantify the scale and structure of the value added to that economy by housing construction and rental activities. To deliver a precise estimate of these various impacts in the Ivorian economy it is critical to access recent, relevant, and accurate data.
The linkages between the production of a particular product (such as a house) and other industries in an economy are usually captured in a Supply and Use Table (SUT), a Social accounting Matrix (SAM), or an Input-Output Table (IOT). According to the study, it is estimated that Côte d’Ivoire’s housing construction value chain could have contributed XOF 1 520 billion (US$2.59 billion) to GDP in 2019. This is estimated to comprise 69 percent intermediate inputs and 31 percent gross value added. In terms of households’ expenditure on housing rents, the study found that this is estimated at XOF 1 539 billion (US$ 2.62 billion). However, it is important to stress that the study was hampered by a lack of current, defensible economic datasets.
How did we reach that estimate?
To reach an optimal level of precision in an economic value chain, there are three important areas that require data: intermediate inputs; gross value-added, and final demand. Intermediate inputs purchased from upstream sectors (group 1 data) focus on data collected from what raw materials, manufactured goods, and services are required to support housing construction and rental activity and which sectors (primary sectors, secondary sectors, or tertiary sectors) are sourced from. Gross value added (group 2 data) represents the sum of indirect taxes subsidies and incomes earned (wages, interest, rent, and profits) by the different factors of production (labour, capital, land, and entrepreneurship) associated with that activity (housing construction or housing rental) in a particular period. The combined impact of these value-adding activities in housing construction and rental and the purchase of intermediate inputs in support of those activities constitute the direct impact of those activities. Lastly, final demand (group 3 data) reflects the scale of domestic expenditure on the output of the focus activity in a particular period. In the case of the housing-related value chains, this will be incorporated into the gross fixed capital formation (GFCF) and final consumption expenditure by households’ elements of GDP estimates using the expenditure method.
Figure 1: conceptual design of an economic value chain
Source: HEVC – Côte d’Ivoire, CAHF 2021
So, what is the state of data collection for each group in Côte d’Ivoire?
During the study process, the Ivorian housing data landscape proved quite challenging in offering us the level of data required to produce a precise estimate of housing in its economy. Group 1 data which would usually be sourced from a SUT, SAM or IOT were not sufficiently recent and sufficiently detailed for this analysis. The most recent SAM that we have been able to find is derived from 2006 data and was published in 2015. It focuses on agricultural production and provides almost no insights into housing construction activities. It can also be argued that given the scale of the structural change that has occurred in Côte d’Ivoire’s economy since 2006 this SAM is unlikely to provide an accurate reflection of current linkages between different activities/sectors of the economy. In the absence of an up-to-date, detailed SAM, the data collected in the housing cost-benchmarking analysis may be a reasonable substitute for the composition of intermediate inputs into the construction value chain. However, this data is not fully inclusive as it captures only “intermediate inputs into formal construction of specified housing typologies in particular urban locations and it does not provide any sense of the scale (number of units) that are produced across the country concerned. The specific omission of data on the informal market is very important in the case of a country like Côte d’Ivoire, where a majority of all households use informal markets to procure or construct housing.” Group 2 data are usually published as part of the national accounts estimates with compositional detail typically obtained from a SUT, SAM or IOT. Unfortunately, these data were not available for CIV. The results of the housing cost benchmarking analysis can be used as a proxy for composition, subject to the same limitations identified in respect of Group 1 data above. Finally, group 3 data is usually derived from published data that reflects the composition of the GFCF by type of asset. In the case of Côte d’Ivoire, the estimates available were not disaggregated in this way. While the weights of Côte d’Ivoire’s CPI do identify a specific share (6.57 percent) of spending on actual rentals, real estate activities, in general, are not separately quantified on the production side of the economy. Instead, they are grouped together in the national accounts with community, social and personal services and the researchers were unable to source data that enabled them to apportion any value added to housing-related real estate activities
The findings of the study were the combination of limited economic information available from the National Institute of Statistics, coupled with regional weights as a proxy as well as available information from the Housing Cost Benchmarking process. “The value chain casts little light on the structure and scale of housing construction, or on the sector composition of intermediate inputs and factor composition of GVA. The principal challenges are first, that we have no way of determining/estimating what share of GFCF relates to housing assets; secondly, what share of construction activity relates to housing construction; and thirdly, that we are – in the absence of a recent and comprehensive SUT – unable to determine the composition of either intermediate inputs or gross value added.”
What should we learn from this experience?
While some countries have an acceptable data availability landscape to build quite detailed SUTs or their equivalent, in other countries data availability and quality is poor, making the building of SUTs either impossible or difficult. In preparing the HEVC Côte d’Ivoire, we learned a lot about the context of housing, the overall economic performance, the trends on the production and expenditures sides of the economy, and could benchmark the costs of new formal construction against other African countries. However, this study points to a critical and crucial deficiency in the development of the Ivorian housing sector — access to housing data. Although we managed to estimate the national housing market contribution to the national economy using various assumptions and alternative data sources, there are significant risks and limitations to this approach. Therefore, we identified two key recommendations to improve the national data landscape:
- We encourage the government of Côte d’Ivoire to urgently design and develop national data instruments to assist with national and sectoral economic impact analysis; and
- We identified the urgent need for the development of a detailed, up-to-date SUT to assist housing and other sectors to improve the analysis of their impacts and working within Côte d’Ivoire’s economy.
Finally, these recommendations, although not exhaustive, could contribute (if implemented) to provide a landscape of access to data on housing and housing finance more conducive to an analysis and understanding of these sectors in terms of housing and housing finance. direct or indirect contribution to the national economy.
 Gardner D. Lockwood K. Pienaar Jacus. (2021). Côte d’Ivoire cost benchmarking and housing economic assessment. CAHF. pg. ii.
 Ibid. Pg. 31.
 Ibid. Pg. ii.
 Ibid. Pg. 33.