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Retail Property Demand Drivers
by Petros S. Sivitanides, Ph.D.1 Retail property is one of the three major types of commercial real estate, that is, properties that are used by businesses.
The other two types include office (office buildings)and industrial real estate (factories, warehouses, distribution centers, logistics centers,etc). Understanding the various factors and forces that drive demand for retail property is very important for investors targeting this type of real estate, as it will help them better evaluate the prospects of a market for supporting the value of their investment. Retail property is demanded by retailers as a means of satisfying household demand for goods and services. Therefore, the aggregate demand for retail property and store space within a market depends on consumption patterns and retail purchases across the different product lines. Since retail space requirements vary considerably across product lines, the correct way to perceive total demand for retail space in a market is to think of it as the sum of differentiated retail space demands by product line. This framework is especially useful as we move from the abstract concept of aggregate demand for retail property in a market to specific locations and smaller-scale areas.
Aggregate demand for retail property depends on the volume of sales by product line and the square feet per dollar of sales required by retailers in each product line in order to operate efficiently. The quantity of sales by product line will depend both on market size and household structure, as well as on other economic forces, as indicated in Figure 28 and listed below:
1) Total population and number of households (size factor)
Before discussing these factors, it is important to understand the overall pattern and path of their influences on an area’s retail sales and demand for retail property. The key factors underlying the volumes and types of retail purchases across product lines are total population size and spending patterns. Spending patterns describe how households/ consumers distribute their purchases across different product lines.
Notice the dynamics described in Figure 28. Spending patterns are determined by two major structural characteristics of the area’s population and households: a) its age mix, and b) its income mix. The age mix of the population refers to the distribution of population and household heads across different age groups. The income mix refers to the distribution of population/households to different income groups in terms of annual income earned from employment or investments. The amount of household income that is available for retail purchases is influenced by various economic factors, such as consumer expectations, credit conditions, and government tax policies.
Figure 28: Retail Demand Drivers
Spending patterns determine the types, price levels, and quantities of goods and services purchased by households, and as such represent a major determinant of demand for retail property in a market. The total quantity of goods and services purchased within each product line are determined by both spending patterns and total population size. The quantities of goods and services sold, along with respective prices, determine the total volume of sales in each product line and the demand for retail space for that product line. Volume of sales and space requirements per dollar of sales determine total space requirements within each product line. The sum of the total retail space requirements by product line represents the aggregate demand for retail space in a market. The dynamics described indicate that the types and volumes of retail goods likely to be demanded in an area determine the types and amount of retail property demanded by retailers operating within that market. Understanding how the factors listed above influence consumer expenditures will help form a frame of reference for identifying circumstances that trigger increases in sales for different product lines and demand for retail retail property.
Total Population and Number of HouseholdsPopulation size is an important determinant of an area's total rental demand for retail property as it affects the magnitude of consumer expenditures in a given area. The total number of households also affects total consumption expenditures since some are household-related as opposed to individual-related. Compare, for example, the case of ten people forming ten households (each one living on its own), with the case of ten people forming five households. Assuming that each household spends the same amount on furniture and kitchen appliances, expenditures on these items in the case of the ten households will be twice as much, compared to the expenditures of the five households. Total expenditures on clothing, though, will be the same in both cases, if we assume that each individual spends the same amount of money on this item. Within this framework population and household size are very important determinants of the level of total demand for retail space and retail property within a market.
Demographic/Age CompositionThe demographic/age composition of an area’s population in terms of age, sex, household size, etc. influences the types of purchases made in that area. Notice that household characteristics and, therefore, the types of goods purchased, change as a household goes through the different stages of the life cycle. For example, young individuals or couples without children (pre-nest stage) form smaller households and spend less on home-related items and more on food away from home. As couples get married and have children (full-nest stage), household size increases, and expenditures related to kids, housing, and food at home increase significantly. As children grow up and move out of the family home, household size gets smaller (empty-nest stage), expenditures get smaller and shift towards items related to older age. Within this context, the age composition of household heads summarizes this information as it captures the different stages of the life cycle. To better understand the impact of demographic composition on spending patterns and demand for retail property, consider the following extreme and unrealistic example of two communities with very different demographic structure. The two communities are Community A, with a population comprised of only young, married couples who own their houses and have kids, and Community B, with a population comprised of only retirees living in rental housing with no kids. Obviously, retail sales in these two communities will differ in many respects. For example, in Community A, there will be many sales of kid-related goods and services (kids clothing, shoes, toys, school items, and so on), as well as housing-related goods (Home Depot), since all households own their houses. On the contrary, in Community B, there will be minimal sales of kid-related goods (gifts for grandkids, perhaps) and limited sales of housing-related goods, since its residents, being renters, refrain from serious home-improvement activities. In a study of differences in retail expenditures per capita across 250 MSAs, Ingene (1984) verified the effect of the different life-cycle stages on consumption patterns. For example, he found that the percentage of households/individuals in the full-nest stage had the greatest positive impact on retail expenditures. In other words, a one-point percentage increase in these households was associated with the greatest percentage increase in retail expenditures. Such impact is consistent with the theory that predicts large expenditures on food, clothing, and durable goods at this stage of the life cycle. Increases in the percentage of households/individuals in the pre-nest stage was also found to contribute to increases in retail sales, but to a lesser extent compared to the full-nest stage effect. This is consistent with the theory that predicts purchases of durables, furniture, automobiles, and clothing by individuals and households at this stage. Not surprisingly, the percentage of households/individuals in the post-nest stage (referring to two life-cycle stages, namely empty nest and solitary survivor) was found to have a negative effect on expenditures per household, specifically as they pertain to department, furniture, and variety stores. The bottom-line conclusion from Ingene’s analysis is that increases in the number of households in the full-nest stage of their life cycle should trigger significant increases in sales of clothing, durables, and food. Such increases will in turn boost demand for store-space within these product lines. On the contrary, increases in the number of persons and households headed by older individuals in the empty-nest and solitary-survivor stages are likely to result in decreases in sales of department, furniture, and variety stores, and therefore, reduction in demand for store-space in these product lines.
Household Disposable IncomeIncome influences significantly spending patterns and retail property demand, since it determines the level of a household’s retail expenditures and the types of goods purchased. The major source of income for most households is employment earnings, which tend to increase, as a person gets older. Thus, household income changes as a household goes through the different stages of the life cycle. For example, single young adults tend to have lower incomes than mature married couples, who are further on the wage scale due to experience and, potentially, earn two incomes, as both husband and wife may have jobs. Furthermore, wages and employment earnings are influenced by economic growth and other economic factors.
The disposable income available to a household for retail purchases is what is left from total income earned after taxes, interest payments, housing expenditures, expenditures on health, education, and public transportation, and savings are taken out. Thus, factors that influence household expenditures on non-retail items have an effect on the level of disposable income available for retail expenditures. These factors are discussed below.
Credit Conditions, Tax Policies, and Relative PricesBesides income and demographics, retail-spending patterns and rental demand for retail property are influenced by factors such as consumer psychology and expectations, credit conditions, tax policies, and relative prices. For example, a thriving economy shapes optimistic consumer expectations, encouraging households to save less and spend more. Macroeconomic shocks that raise fears or hopes of potential negative or positive effects on the economy may also affect consumer expectations and sentiment. According to data drawn from the University of Michigan Survey of Consumers, and the Survey of Current Business (published by the US Department of Commerce), both consumer confidence and real consumption (spending adjusted to take into account increases in the prices of goods and services) registered sharp declines in August 1990, when Iraq invaded Kuwait (Abel and Bernanke, 1995). Consumer spending patterns are also influenced by the availability of consumer credit and by tax policies. Higher availability of consumer credit is usually reflected in lower interest rates, which stimulate household borrowing either through home-equity financing and re-financing or unsecured loans. This increased borrowing translates to higher disposable income, which leads to increased spending and larger volume of retail sales. Tax policies influence the level of a household’s disposable income, too. For example, decreases in tax rates reduce tax payments and contribute to increases in disposable income. Finally, sizable changes in relative prices may also affect the distribution of consumer expenditures across the different retail categories. For example, steep increases in the prices of necessity goods will force many households to devote a considerably higher share of their budget on these items and reduce the share spent on non-necessity goods and services.
Required Square Feet per Dollar of Sales
The required square feet per dollar of sales represent the amount of space needed by a storekeeper within a given product line, in order to operate efficiently. In retail terminology, this indicator is actually measured by sales-per-square-foot norms (actual data on sales per square foot by type of store and shopping center can be found in the "Dollars and Cents of Shopping Centers"), which represent the average sales per square foot for stores selling a particular type of good or service. This format of measuring the relationship between sales and required space is more practical for analytical purposes, since typically, the analyst will have an estimate of total potential sales, which needs to be converted into square feet requirements. This can be done easily by simply dividing the expected volume of sales in dollars by the appropriate sales-per-square-foot norm. For example, consider a particular line of trade, for which the average sales per square foot is $200 per square foot. Consider also that, according to an analysis of the area’s sales potential, total sales in this particular product line are expected to reach $20 million. Therefore, the total store-space needed can be estimated as the ratio of $20 million over $200, which yields 100,000 square feet. Sales-per-square-foot norms vary considerably, depending on the type of store and the setting in which the store is located. For example, a stand-alone furniture store may have fewer sales per square foot than a furniture store within a regional mall. Data on sales-per-square foot norms are published by the Urban Land Institute for each type of store/product line in different shopping center formats, since such norms vary considerably across these dimensions. Analysts and investors should be very cautious of the sales-per-square foot norm used in estimating the total demand for retail property in a market as it could lead to a considerable overestimation of such demand if the figure used is higher than what the market would allow. For this reason retail property investors and developers are advised to examine alternative retail space demand estimates based on both pessimistic and optimistic sales-per-square-foot assumptions. 1This is an excerpt from the book Profitable Real Estate Investing: A Value Growth Approach by Petros S. Sivitanides, Ph.D. Related Posts Books on Retail Property and Development Shopping Centers Retail Location Return from Retail Property to Property Investing
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