Skip to main content

Housing Demand, Affordability and Mortgage Financing: A Case Study of Karachi

  • Conference paper
  • First Online:
Sustainability and Financial Services in the Digital Age (EUROCRYPT 1988)

Abstract

Karachi is the largest city of Pakistan amongst the mega metropolitan of the world. Since the independence of Pakistan, in 1947, the city has undergrown massive surge in population largely contributed by migration and urbanization. The amount of housing units present in Karachi is insufficient for the ever-growing population of the city and the housing units present are unaffordable to most of the public, especially the bottom 20% of the income group. The purpose of this study is to analyze the factors that contribute to the variation of local construction costs which in turn affect the affordability of housing units present in Karachi, evaluate affordability and mortgage repayment ability of different income groups present in the city and lastly, forecast housing demand for the city. To fulfill the objectives of this study, secondary data from House Building Finance Corporation (HBFC), Pakistan Bureau of Statistics (PBS), Household Integrated Economic Survey of Pakistan (HIES) was collected, and primary data was also collected from local construction contractors and house builders in Karachi. This data was used to determine construction rates in different localities of Karachi which is shown in the table below. The ARIMA methodology was used to find the future demand of housing in Karachi, as shown in the figure, and House Price to Income Ratio, Mortgage to Income Ratio and Residual Income Approach was used for the analysis of affordability. Housing demand was forecasted for the years 2017–2020 which clearly shows that the housing demand is increasing with respect to time, and the Mean Absolute Percentage Error (MAPE) was 2.07%. The analysis of affordability and mortgage repayment shows that the bottom 20% of income earners cannot afford a house or pay off their mortgages.

Construction rates in different towns of Karachi

Area

Rates

D. H. A

2200/ft2

Gulistan-e-Johar

1300/ft2

Gulshan-e-Iqbal, North Karachi & Safoora Goth

1600/ft2

Malir, Shah Faisal, Korangi, Landhi

1250/ft2

A line graph compares the rising trends of the forecasted series and the original series. The trend started at 1500000 in 2000 and ended at almost 3500000 in 2015. The forecast for 2020 is 4000000.

Trend line of original and forecasting housing demand

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Ahmed, A. (2015). Housing demand in urban areas of Pakistan. Department Of Economics Pakistan Institute of Development Economics, Islamabad, Pakistan Research Repository.

    Google Scholar 

  • Ahmed, A. (2022). Evaluation of Naya Pakistan housing. In F. Zulfiqar (Ed.), Evaluations of regulatory authorities, government packages, and policies (p. 43). Pakistan Institute of Development Economics.

    Google Scholar 

  • Ali, A. (2008). The impact of financial crisis on Pakistani economy. Strategic Studies, 28, 106–117.

    Google Scholar 

  • Al-Marwani, H. A. (2014). An approach to modeling and forecasting real estate residential property market. Doctoral dissertation.

    Google Scholar 

  • Aziz, T. (2021). Changes in land use and ecosystem services values in Pakistan, 1950–2050. Environmental Development, 37, 100576.

    Article  Google Scholar 

  • Baig, M. S. R., Nawaz, H. M. U., & Idrees, R. Q. (2020). Housing for all: A case study of Kachi Abbadis (slums) in achieving the goal of housing for all in Pakistan. Orient Research Journal of Social Sciences, 5, 32–44.

    Google Scholar 

  • Basit, M., Sajjad, S. H., Khan, M. I., Ali, A., & Kurshid, S. K. (2018). Spatio-temporal trends of urban population in Pakistan. Asian Journal of Multidisciplinary Studies, 6(8), 21–26.

    Google Scholar 

  • Beaver, W., & Morse, D. (1978). What determines price-earnings ratios? Financial Analysts Journal, 34(4), 65–76.

    Article  Google Scholar 

  • Chang, Y. W., & Liao, M. Y. (2010). A seasonal ARIMA model of tourism forecasting: The case of Taiwan. Asia Pacific Journal of Tourism Research, 15(2), 215–221.

    Article  Google Scholar 

  • Clayton, J. (2009). Thinking spatially: towards an everyday understanding of inter-ethnic relations. Social & cultural geography, 10(4), 481–498.

    Google Scholar 

  • Collinson, R., Ellen, I. G., & Ludwig, J. (2015). Low-income housing policy. In Economics of means-tested transfer programs in the United States (Vol. 2, pp. 59–126). University of Chicago Press.

    Google Scholar 

  • Daud, N. M., Nor, N. M., Ali, U. N., Yusof, M. A., & Munikanan, V. (2017). Affordable housing system: A review on issue of housing affordability. The Social Sciences, 12(7), 1281–1287.

    Google Scholar 

  • De Risi, R., De Paola, F., Turpie, J., & Kroeger, T. (2018). Life Cycle Cost and Return on Investment as complementary decision variables for urban flood risk management in developing countries. International Journal of Disaster Risk Reduction, 28, 88–106.

    Article  Google Scholar 

  • Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10, 1847979018808673.

    Article  Google Scholar 

  • Freybote, J. (2019). Real estate markets in Pakistan. In Real Estate in South Asia (pp. 277–290). Routledge.

    Chapter  Google Scholar 

  • Gan, Q., & Hill, R. J. (2009). Measuring housing affordability: Looking beyond the median. Journal of Housing Economics, 18(2), 115–125.

    Article  Google Scholar 

  • Hanif, F., Shamshir, M., & Alwi, K. K. (2020). House finance for low income groups in Pakistan. Independent Journal of Management & Production, 11(4), 1394–1418.

    Article  Google Scholar 

  • Hashmi, S., Safdar, N. F., Zaheer, S., & Shafique, K. (2021). Association between dietary diversity and food insecurity in urban households: A cross-sectional survey of various ethnic populations of Karachi, Pakistan. Risk Management and Healthcare Policy, 14, 3025–3035.

    Article  Google Scholar 

  • Hulchanski, J. D. (1995). The concept of housing affordability: Six contemporary uses of the housing expenditure-to-income ratio. Housing Studies, 10(4), 471–491.

    Article  Google Scholar 

  • Januschowski, T., Gasthaus, J., Wang, Y., Salinas, D., Flunkert, V., Bohlke-Schneider, M., & Callot, L. (2020). Criteria for classifying forecasting methods. International Journal of Forecasting, 36(1), 167–177.

    Article  Google Scholar 

  • Javed, N., & Sani-e-Zahra Naqvi, S. (2022). Affordable housing in Pakistan: The policy and institutional imperatives. In A. Kundu, T. P. Dentinho, H. Magsi, K. Basu, & S. Bandyopadhyay (Eds.), Accessible housing for South Asia: Needs, implementation and impacts (pp. 225–242). Springer.

    Chapter  Google Scholar 

  • Kam, K. M. (2014). Stationary and non-stationary time series prediction using state space model and pattern-based approach. The University of Texas at Arlington.

    Google Scholar 

  • Khadduri, J., & Wilkins, C. (2008). Designing subsidized rental housing programs: What have we learned? In N. P. Retsinas & E. S. Belsky (Eds.), Revisiting rental housing: Policies, programs, and priorities (pp. 161–190). Brookings Institution Press.

    Google Scholar 

  • Kramna, E. (2014). Key input factors for discounted cash flow valuations. WSEAS Transactions on Business and Economics, 11, 454–464.

    Google Scholar 

  • Kutty, N. K. (2005). A new measure of housing affordability: Estimates and analytical results. Housing Policy Debate, 16(1), 113–142.

    Article  Google Scholar 

  • Malik, S., Roosli, R., & Tariq, F. (2020). Investigation of informal housing challenges and issues: experiences from slum and squatter of Lahore. Journal of Housing and the Built Environment, 35(1), 143–170.

    Article  Google Scholar 

  • Malik, S., Roosli, R., Tariq, F., & Yusof, N. A. (2020). Policy framework and institutional arrangements: Case of affordable housing delivery for low-income groups in Punjab. Pakistan. Housing Policy Debate, 30(2), 243–268.

    Article  Google Scholar 

  • Melese, M. (2004). City expansion, squatter settlements and policy implications in Addis Ababa: The case of Kolfe Keranio sub-city. Ethiopian Journal of the Social Sciences and Humanities, 2(2), 50–79.

    Google Scholar 

  • Nassif, A. B., Soudan, B., Azzeh, M., Attilli, I., & AlMulla, O. (2021). Artificial intelligence and statistical techniques in short-term load forecasting: a review. arXiv preprint arXiv:2201.00437.

    Google Scholar 

  • Ong, C. S., Huang, J. J., & Tzeng, G. H. (2005). Model identification of ARIMA family using genetic algorithms. Applied Mathematics and Computation, 164(3), 885–912.

    Article  Google Scholar 

  • Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T., & French, N. (2003). Real estate appraisal: a review of valuation methods. Journal of Property Investment & Finance, 21(4), 383–401.

    Article  Google Scholar 

  • Pogge, T. (2010). World poverty. In J. Skorupski (Ed.), The Routledge companion to ethics (pp. 796–807). Routledge.

    Google Scholar 

  • Rhea, S., Wang, E., Wong, E., Atkins, E., & Storer, N. (2017, May). Littletable: A time-series database and its uses. In Proceedings of the 2017 ACM international conference on management of data (pp. 125–138).

    Google Scholar 

  • Rizvi, M. A. (2019). The ethics of staying: Social movements and land rights politics in Pakistan. Stanford University Press.

    Google Scholar 

  • Saavedra, J., & Chong, A. (1999). Structural reform, institutions and earnings: evidence from the formal and informal sectors in urban Peru. The Journal of Development Studies, 35(4), 95–116.

    Article  Google Scholar 

  • Samuel, P., & Nisar, S. (2021). Stuck in slums: A case study of slums in Islamabad Pakistan. European Scientific Journal, 17(2), 56–78.

    Google Scholar 

  • Sathar, Z. A. (2012). Pakistan’s population prospects, 2010–2030: A glass half full or half empty? In H. Groth & A. Sousa-Poza (Eds.), Population dynamics in Muslim countries: Assembling the jigsaw (pp. 79–95). Springer.

    Chapter  Google Scholar 

  • Shumway, R. H., Stoffer, D. S., & Stoffer, D. S. (2000). Time series analysis and its applications (Vol. 3, p. 4). New York: springer.

    Google Scholar 

  • Soomro, F. A., Memon, M. J., Chandio, A. F., Sohu, S., & Soomro, R. (2019). Causes of time overrun in construction of building projects in Pakistan. Engineering, Technology & Applied Science Research, 9(1), 3762–3764.

    Article  Google Scholar 

  • Stone, P. R. (2006). A dark tourism spectrum: Towards a typology of death and macabre related tourist sites, attractions and exhibitions. Tourism: An International Interdisciplinary Journal, 54(2), 145–160.

    Google Scholar 

  • Tariq, F., Salman, M., Hasan, J., Zafar, Z., Malik, S., Nawaz, M., Gul, A., & Sheikh, N. B. (2018). Appraisal of national housing policy—A case of Pakistan. Technical Journal, 23(03), 1–8.

    Google Scholar 

  • Tirmizi, M. A. (2007). Sustainable urban development strategies for the provision of low-income housing in Pakistan. In CESB 07 PRAGUE conference.

    Google Scholar 

  • Ullah, A. (2016). Prospects of smart cities development in India through public private partnership. International Journal of Research in Advent Technology, 4(1).

    Google Scholar 

  • Umair, M., & Naz, L. (2019). The index value trend analysis of rural-urban migration in Pakistan: The current and future perspectives. Pakistan Journal of Social Sciences, 39(3), 979–993.

    Google Scholar 

  • Van Der Gaag, M., & Snijders, T. A. (2005). The resource generator: Social capital quantification with concrete items. Social Networks, 27(1), 1–29.

    Article  Google Scholar 

  • Whittle, P. (1953). Estimation and information in stationary time series. Arkiv för matematik, 2(5), 423–434.

    Article  Google Scholar 

  • Zulkifi, Z., & Bujang, A. A. (2008). Housing affordability: A study on household expenditures ratio on the income for the lower and middle.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Uneb Gazder .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Khan, R.A., Gazder, U. (2024). Housing Demand, Affordability and Mortgage Financing: A Case Study of Karachi. In: Mansour, N., Bujosa Vadell, L.M. (eds) Sustainability and Financial Services in the Digital Age. EUROCRYPT 1988. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-67511-9_16

Download citation

Publish with us

Policies and ethics