In Pakistan, Reall took the Market Shaping Indicators framework developed with the Centre for Affordable Housing Finance in Africa (CAHF) and worked with Impetus Advisory Group to adapt it for the Pakistani context. The data on narrative on this page is a result of this work, which was conducted in the cities of Karachi, Lahore, Rawalpindi-Islamabad, Quetta, and Peshawar, accounting for 15% of the total population of Pakistan.
Implementing the MSI framework forms a core part of Realls strategy in Pakistan, which involves investing in 6,500 affordable homes, enabling 1.2 million people to access housing finance, and promoting policy and regulatory change positively impacting 0.8 million people.
Country Overview
In recent years, Pakistan has witnessed a nation-wide housing crisis. Between the 1998 and 2017 national censuses, the countrys population increased by 57%, while the increase in urban population was 75.6% . This increase has created an urban housing demand of up to 350,000 houses per annum, 87% of which is from lower-middle income groups. In contrast, the supply of urban housing is restricted to only 150,000 houses per annum, creating a shortfall of 200,000 per annum that gets further compounded year-on-year, leading to inflationary pressure on property prices.
Traditionally, housing development projects have a higher incentive to focus on upscale urban housing, owing to the larger margins in transactions, and tend to deprioritize urban dwellings that would cater to the Bottom 40% of the income pyramid (B40). Recent efforts by the government to mandate developers to allocate a minimum percentage of housing projects to affordable dwellings for the B40 have produced limited results “ as housing permits are still issued by the regulator without due consideration to this important requirement. Furthermore, without government subsidies on land purchase, lower-middle income groups, a large slum population in the form of katchi abadis exist and are upheld within Pakistan. These urban slums are built on government land or agricultural land and they currently serve approximately a quarter of the total unmet demand for affordable housing. Moreover, 40 % of these slums are constructed through further densification of inner-city areas .
According to the 2019 “ 2020 Pakistan Economic Survey, the real estate and construction sectors employ a combined 8% of the total labour force in the country, making it the fourth largest employer . Despite its growing role in the local economy, the real estate sector is marked by a lack of accurate information, regulatory oversight, innovative investment and financing methods/products, along with an archaic bureaucratic processes. The local property market is highly volatile and data reveals strong inflationary trends in urban real estate prices. According to zameen.com , nationwide house prices (in nominal terms) rose by 5.05% to PKR 10,875 per square feet (sq. ft) during Q1 of 2019. However, when adjusted for inflation over the same period, house prices dropped by 3.98%.
State of Housing Data
Despite being one of Pakistans cornerstones of local investment and urban employment, the traditional real estate sector is still not sufficiently documented. Policymakers are unaware of the true costs and revenues realized by local development agencies, with most residential land undervalued in sale documentation to evade applicable taxes. Concurrently, construction firms and developers rely more on market speculation than end-user utility when designing and launching real estate projects. This information asymmetry across policymakers, developers, financiers, and end-users creates arbitrage opportunities and encourages price inflation in residential land. Information is the true currency of the real estate market in Pakistan and is closely guarded by the markets agents. Not surprisingly, accurate, relevant, and credible data on land prices, construction costs and housing units is rare and limited.
As a subset of the larger real estate landscape, affordable housing is no exception to this state of data availability and accessibility. Household surveys and censuses rarely reference affordable housing as a domain of interest when collecting citizen data, and policy makers are usually restricted to project-specific, supply-side data only. Latent information on affordable housing metrics can be gleaned from a few official databases collected by the national and provincial bureaus of statistics, and a few private real estate brokerage houses have internal databases of relevance. However, data accessibility is still an issue for private datasets, as these data are rarely accessible nor open for dissemination to a wider audience.
% of urban bottom 40 households without access to basic sanitation services
Country | Year | Data Source | Value |
---|---|---|---|
Cote d'Ivoire | 2012 | DHS | 96.5% |
Ghana | 2014 | DHS | 93.15% |
Kenya | 2014 | DHS | 88.25% |
Morocco | 2004 | DHS | 52.05% |
Mozambique | 2011 | DHS | 95.6% |
Nigeria | 2018 | DHS | 83.1% |
Tanzania | 2017 | DHS | 37% |
Uganda | 2016 | DHS | 94.5% |
Rwanda | 2016 | National Institute of Statistics Rwanda (NISR) | 13.13% |
Pakistan | 2018 | The DHS Program | 2.75% |
India | 2018 | NSSO 76th Round | 0.2% |
% of urban population living in slums, informal settlements, or inadequate dwellings
Country | Year | Data Source | Value |
---|---|---|---|
Cote d'Ivoire | N/A | ||
Ghana | N/A | ||
Kenya | N/A | ||
Morocco | N/A | ||
Mozambique | N/A | ||
Nigeria | N/A | ||
Tanzania | N/A | ||
Uganda | N/A | ||
Rwanda | 2018 | World Bank | 42.1% |
Pakistan | N/A | ||
India | 2018 | NSSO 76th Round | 35% |
Number of residential mortgages outstanding
Country | Year | Data Source | Value |
---|---|---|---|
Kenya | 2019 | Central Bank of Kenya | 27,993 |
Nigeria | 2019 | NMRC | 32,260 |
Tanzania | 2019 | Bank of Tanzania and Tanzania Mortgage Refinance Company Limited | 5,460 |
Rwanda | 2020 | National Bank of Rwanda (NBR) | 44,177 |
Pakistan | 2019 | State Bank of Pakistan - Housing Finance Data Review | 58,620 |
India | 2020 | Reserve Bank of India | 9,817,180 |
Price of the cheapest, newly built dwelling by a formal developer or contractor
Country | Year | Data Source | Value |
---|---|---|---|
Cote d'Ivoire | 2018 | Site d'annonce et promotion dans l'immobilier en Côte d'Ivoire | 15,500,000 CFA$27,087.48 |
Ghana | 2019 | Damax Construction Co. Ltd | 108,704 GH₵$19,621.66 |
Kenya | 2019 | Tsavo Real Estate | 4,000,000 Ksh$37,037.04 |
Morocco | 2019 | Various real estate websites | 250,000 DH$27,027.03 |
Mozambique | 2016 | Casa Minha | 3,418,491 MZ$48,147.76 |
Nigeria | 2019 | Millard Fuller Foundation; Shelter Origins | 2,900,000 NGN$7,651.72 |
Tanzania | 2018 | CAHF | 37,966,107 TZS$16,508.58 |
Uganda | 2019 | Various property developers | 125,000,000 UGX$34,097.11 |
Rwanda | 2020 | Marchal Real Estate Developers | 10,000,000 R₣$11,119.14 |
Pakistan | 2021 | Partners | 2,500,000 PKR$14,305.33 |
India | 2022 | Real estate websites and industry experts | 160,000 IN₹$2,176.87 |
% of national households that rent their dwelling
Country | Year | Data Source | Value |
---|---|---|---|
Ghana | 2017 | Ghana Statistical Service | 28% |
Kenya | 2019 | Central Bank of Kenya, Kenya National Bureau of Statistics, FSD Kenya | 35.01% |
Morocco | 2014 | High Commission for Planning; World Bank | 18.5% |
Nigeria | 2018 | World Bank; Nigeria National Bureau of Statistics | 21.8% |
Tanzania | 2017 | National Bureau of Statistics | 80.56% |
Uganda | 2016 | DHS | 53.45% |
Rwanda | 2020 | Access to Finance Rwanda (AFR) and National Institute of Statistics Rwanda (NISR) | 8.94% |
Pakistan | 2017 | Population and Housing Census | 11.53% |
India | 2018 | NSSO 76th Round | 13% |
Ease of Doing Business Index Rank: Global
Country | Year | Data Source | Value |
---|---|---|---|
Cote d'Ivoire | 2020 | World Bank | 110 |
Ghana | 2020 | World Bank | 118 |
Kenya | 2019 | World Bank Ease of Doing Business | 61 |
Morocco | 2020 | World Bank | 53 |
Mozambique | 2019 | World Bank | 74 |
Nigeria | 2020 | World Bank | 131 |
Tanzania | 2020 | World Bank | 141 |
Uganda | 2020 | World Bank | 116 |
Rwanda | 2020 | World Bank Ease of Doing Business Indicators | 38 out of 190 |
Pakistan | 2020 | World Bank Doing Business Indicator | 108 out of 190 |
India | 2020 | World Bank | 63 out of 190 |
GDP Per Capita
Country | Year | Data Source | Value |
---|---|---|---|
Cote d'Ivoire | 2018 | World Bank | 1,024,171 CFA$1,789.82 |
Ghana | 2019 | World Bank | 11,489 GH₵$2,073.83 |
Kenya | 2018 | World Bank | 173,272 Ksh$1,604.37 |
Morocco | 2018 | World Bank | 30,725 DH$3,321.62 |
Mozambique | 2018 | World Bank | 30,772 MZ$433.41 |
Nigeria | 2018 | World Bank | 659,159 NGN$1,739.21 |
Tanzania | 2018 | National Bureau of Statistics; World Bank | 2,297,020 TZS$998.80 |
Uganda | 2018 | World Bank | 2,357,327 UGX$643.02 |
Rwanda | 2019 | World Bank | 737,578.59 R₣$820.12 |
Pakistan | 2020 | World Bank National Accounts Data | 188,900 PKR$1,080.91 |
India | 2020 | Ministry of Statistics and Program Implementation | 151,760 IN₹$2,064.76 |
Population Size
Country | Year | Data Source | Value |
---|---|---|---|
Cote d'Ivoire | 2017 | World Bank | 24,437,469 |
Ghana | 2019 | World Bank | 30,417,856 |
Kenya | 2017 | World Bank | 50,221,473 |
Morocco | 2017 | World Bank | 36,471,769 |
Mozambique | 2018 | World Bank | 29,495,962 |
Nigeria | 2017 | World Bank | 190,873,311 |
Tanzania | 2019 | World Bank | 58,005,463 |
Uganda | 2017 | World Bank | 41,487,000 |
Rwanda | 2019 | World Bank | 12,626,950 |
Pakistan | 2020 | World Bank National Accounts Data | 220,892,331 |
India | 2021 | Minsitry of Health and Family Welfare | 1,361,343,000 |
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The Market Shaping Indicators project is a work in progress. Significant gaps exist in data, which will be filled in future revisions. We would recommend checking back regularly for updates. We are keen to receive any feedback that you have on this Dashboard, which can be sent to [email protected].
Using the Dashboard
The indicators are split into 6 key areas, split into the Housing Value Chain: Land & Infrastructure, Construction & Investment, Sales & Rental, Maintenance & Management, Enabling Environment, Economic Environment and Demand, shown in the following tabs. Navigation can either be undertaken by using the tabs, or through the Search box immediately above. Above this, currency indicators can be toggled between USD and local currency.
Users are able to further interrogate each indicator each indicator through clicking on the arrows to the left of each indicator. This expanded section shows the data elements that are used to produce the overall indicator value, dates of data collection, source details, hyperlinks to the original data where possible, and a breakdown of data quality. The majority of indicators are quality assessed, based on the whether they are: Interpretable; Relevant; Sufficiently Accurate; Representative; Timely; and Accessible. Indicators are scored on each of these criteria using a 1-4 star system, detailed below:
☆ – poor
☆☆ – moderate
☆☆☆ – good
☆☆☆☆ – excellent
Finally, all data can be downloaded for further interrogation. By clicking on Switch to Data View at the top of the screen, users can filter data based on countries and columns, and download in a .csv or .xls file.
Bottom 40
Reall targets the Bottom 40% of the urban income pyramid, referred to as the ‘Bottom 40’ or ‘B40’. An objective of the MSI work was to better understand and demonstrate the market from the perspective of households in the Bottom 40, and as such data is aggregated for this group where possible. Data for this group can be particularly challenging to come across. In part, this is due to the difficulties in accurately defining this group using existing data sets. Additionally though, the informality of much of life for lower income groups severely limits data availability, particularly in terms of key data on jobs, housing and relationships with local government. This lack of data is a key blockage for further engagement at the lower end of the housing market, and resolving this is an objective of Reall’s and of the MSI work.
Aggregations
Data is shown at various different “aggregations”, which demonstrate the size and location of the population for which the data represents. This varies from national to city level in terms of population groupings. Additional aggregations exist for the Bottom 40, as detailed above, enabling a focused view on the lower end of the market.
For relevant data, Reall’s partners are also included as an aggregation. This is not meant to be representative of the entire market, but recognises that as practitioners and experts within the lower end of the housing market of each country, their experiences are a useful check on other data sets, and an indication of the value when other data is not available.
Terms of Use
Reall Ltd (“Reallâ€) endeavours to make its data as freely available as possible in order to demonstrate the successes of its model and encourage other actors into the affordable homes movement. Reall provides the user with access to these data free of charge subject to the terms of this agreement.
Users are encouraged to use the data to benefit themselves and others in creative ways.
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Until 2018, land prices in Pakistan were officially set by the Government based on District Commissioner (DC) rates “ rates registered with the Property Registration Authorities but usually set lower (5 to 10 times) than the prevailing market prices. More recently, the Federal Board of Revenue (FBR) has introduced property valuation tables (published by the District Collector at the Revenue Department) that help inform the DC rates and ensure that disparities between these rates and prevailing market rates are minimized. In theory, these updated DC rates also inform the computation of accurate withholding tax on the sale/purchase of property and capital gains tax. In fact, however, the implementation of these updated DC rates has been challenging, owing to resistance from the existing network of property dealers and their clients who claim that the true essence of the Stamp Rules Act of 1899 requires consultation with the public when setting the DC rates. However, the Act itself is due for major revision as it relates to single-story dwellings and does not include any provision for upper-portion taxation rates.
Furthermore, existing physical designs for affordable housing are inadequate for addressing the effects of rapid urbanization. Existing housing remains susceptible to risk, especially in lieu of environmental challenges. Approximately 10% of otherwise durable houses did not have access to adequate services, a figure which reflects poorly on previous housing efforts.
Looking closely at indicators within the land title and assembly sub-domain revealed that while data values were available, they did not fare well from a quality standpoint. Future work requires stronger developer interaction to be able to effectively improve data quality and reliability. As an example, while data pertaining to property registration procedures is indicated on land authority portals, conversations with Reall partner firms revealed that values were not always reflective, with property registration processes, time and costs often much higher than stipulated and would require active intervention and follow-ups. While official provincial department portals indicated that it would range between 11-14 days to register residential property, Reall partner firms stated that it would require an average of 105 days for the same indicator.
Of the 25 indicators in this group, 24 are currently populated.