পদ্ধতির তুলনা করুন

	Housing Affordability Index	Urban Scaling Laws
ক্ষেত্র	Urban Studies	Urban Studies
পরিবার	Process / pipeline	Regression model
উদ্ভবের বছর	2006	2007
প্রবর্তক	Housing-economics tradition (ratio measures); Michael E. Stone (residual-income approach)	Luís Bettencourt & Geoffrey West
ধরন	Index/ratio comparing housing cost to household income	Power-law regression of urban indicators against population size
মৌলিক উৎস	Stone, M. E. (2006). What is housing affordability? The case for the residual income approach. Housing Policy Debate, 17(1), 151–184.	Bettencourt, L. M. A., Lobo, J., Helbing, D., Kühnert, C., & West, G. B. (2007). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences, 104(17), 7301–7306.
অপর নাম	Median Multiple, Housing Cost Burden Ratio, Residual Income Affordability, NAR Housing Affordability Index	Urban Scaling, Settlement Scaling Theory, Power-Law Urban Scaling, Superlinear and Sublinear Urban Scaling
সম্পর্কিত	4	4
সারসংক্ষেপ	A housing affordability index summarises how the cost of housing in a city or market relates to what households can pay, condensing prices, rents and incomes into a single interpretable number. The simplest forms are ratios — the median house price divided by median income, or housing outlays as a share of income — while the residual-income approach championed by Michael Stone instead asks what is left for everything else after housing is paid. Together these measures let analysts compare affordability across places and over time, flag cost-burdened populations, and track housing stress as markets shift.	Urban scaling laws describe how the aggregate properties of cities — wealth, innovation, infrastructure, crime — change systematically with population size, following power laws rather than growing in simple proportion. Building on the 2007 work of Luís Bettencourt, Geoffrey West and colleagues, the framework shows that socioeconomic outputs typically scale superlinearly (a doubling of population more than doubles GDP and patents) while infrastructure scales sublinearly (larger cities need proportionally fewer roads and cables per person), with a single exponent β capturing the regularity across an entire urban system.