Analyzes the price elasticity of the supply of rental housing services, defined as the percentage increase in supply associated with a one percent increase in price. The purpose of the report is to predict the price changes associated with supply responses to shifts in demand. Section II analyzes each component of supply response separately. It presents price elasticities for the repair, inventory, and occupancy responses to demand shifts. It reviews the literature on all three and offers new estimates for the second and third (the estimates are based on the analysis of Annual Housing Survey data from the U.S. Census Bureau reported in Appendix B). Section III combines the three individual supply elasticities into a composite elasticity. It accomplishes the integration using a model of housing-market responses to demand shifts presented in Appendix C. The model was built during the Housing Assistance Supply Experiment to explain the housing market's response to demand shifts caused by an experimental housing allowance program.

Research results for short-term and long-term petroleum elasticities are summarized, and existing Energy Information Administration (EIA) models of energy demand are used to develop estimates of price response for 1-, 3-, 5-, and 10-year intervals. In the short-run, elasticities reported for most petroleum products in most end-uses generally range from -.1 to -.4 although the numerous research estimates for gasoline demand elasticity are clustered in the more elastic range of -.1 to -.3. EIA models used in this analysis fall within these ranges and tend toward the higher (in absolute terms) end of the elasticity range. In transportation uses, for which most of the research has centered on gasoline, petroleum demand has been shown to be less responsive to price than the other sectors, with long-term gasoline estimates generally falling in the range of -.3 to -.9. In investigating the price sensitivity for periods up to 10 years using the EIA Demand Analysis System, petroleum product elasticities in all sectors are typically between -.4 and -.1. For automobile gasoline demand, the greatest proportion of the 10-year price response is manifested in increased cutbacks in travel. The model studies show that, given continued increases in the price of oil, the proportion of consumer budgets as well as industrial production costs allocated for petroleum products will increase; that petroleum prices will be volatile in instances of temporary oil shortages; and that market forces can achieve long-term conservation of petroleum, but at the cost of greater proportional increases in oil prices.

Published estimates of the price elasticity of residential electricity demand range from -0.29 to -0.70, for analyses based on household level data; however, the area level estimates from range from -0.02 to -0.15. A similar pattern has been reported for estimates of the income elasticity of residential demand for electricity. Each published study relied on one type of data set (aggregated or disaggregated) and these datasets cover different time periods and locations. This raises the question: does the pattern generated by the published results reflect systematic differences generated by the use of aggregated vs. disaggregated data, or does the pattern reflect random variations in the study settings? In this research the hypothesis has been tested that the pattern generated by the published results reflects the use of aggregated vs. disaggregated data, by constructing both an individual-level dataset and a county-level dataset for one state (State of Nevada) covering the period from 2005 to 2011. Both datasets have been used to estimate household and utility level price and income elasticities of residential demand for electricity. This research shows the same pattern reported in the published studies: the magnitude of the estimated price elasticity generated by the disaggregated data exceeds the magnitude of the estimate generated by the disaggregated data. However, the magnitudes of the two income elasticities do not follow the same pattern.

Despite its importance for policy purposes (including climate policy and the energy transition), evidence about the price elasticity of natural gas demand in the residential sector is very limited and based on inference from situations with modest variation in prices. We focus on a locale and time when price changes were extreme and presumably salient to consumers, namely Ukraine between 2013 and 2017. We exploit the tariff reforms and detailed micro-level household consumption records to estimate the price elasticity of the demand for natural gas. To isolate behavior, attention is restricted to those households that made no structural energy-efficiency upgrades to their homes, and thus kept the stock of gas-using capital fixed. We further examine the short-run elasticity by restricting the sample to a few months before and after the tariff changes. Our results suggest that under extreme price changes, households are capable of reducing consumption, even without installing insulation or making any other structural modifications to their homes. The price elasticity is about -0.16. Wealthier households, people living in multifamily buildings, and heavy users have more inelastic demands. Households reduced consumption even when they received "subsidies," namely lump-sum government assistance, suggesting that when the price signal is sufficiently strong, lump-sum transfers have only a minimal effect on consumption. We also find some evidence that the stronger the salience, the stronger the responsiveness to price, although this effect is modest and may partly overlap with that of income or baseline consumption. Our data also suggest that the consumers with the lowest uptake of energy efficiency improvements might be those who−by necessity or through skills−are the most productive at reducing energy use through behaviors.