विधियों की तुलना करें

	Housing Affordability Index	Urban Scaling Laws
क्षेत्र	Urban Studies	Urban Studies
परिवार	Process / pipeline	Regression model
उद्भव वर्ष	2006	2007
प्रवर्तक	Housing-economics tradition (ratio measures); Michael E. Stone (residual-income approach)	Luís Bettencourt & Geoffrey West
प्रकार	Index/ratio comparing housing cost to household income	Power-law regression of urban indicators against population size
मौलिक स्रोत	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.
उपनाम	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
संबंधित	4	4
सारांश	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.