Several Alternative Indicators Show Strong Predictive Value for U.S. Hotel Demand

Traditional indicators such as GDP, inflation, wage growth, and unemployment have long guided hotel demand forecasts. However, uneven economic conditions across income groups and traveler segments are weakening their effectiveness. Alternative inputs tied more directly to household finances and behavior may now provide stronger predictive value.

STR performed this analysis focused on chain scale demand. The relationships identified here are not measurements of direct trends or demand performance. Rather, the analysis evaluates which economic and consumer variables appear most closely associated with changes in hotel demand across individual scales. Because each scale serves a different customer base and demand mix, it should not be surprising that the strongest explanatory variables vary by chain scale. 

When forecasting demand, the general rule-of-thumb is that correlations above +0.4 and below -0.4 are considered strong enough to provide additional predictive value. 

The former represents a positive correlation. As the predictor variable increases, so does demand. The latter represents the opposite. As the input variable increases, demand decreases. 

A zoomed-out view of these relationships shows that positive correlation-variables are typically associated with tailwind pressures, while negative correlation-variables are aligned with headwind pressures. 

Middle tiers show strongest correlations

In the case of traditional macroeconomic indicators, the middle of the market—Upscale and Upper Midscale—exhibits the strongest correlations. 

•    The Upscale segment has correlations around 0.83. 
•    The relationships are slightly weaker for Upper Midscale with input correlations around 0.74. 
•    For both segments, these values would indicate broad spectrum strength with these typical variables. 

The primary reason for these relationships is the demographic composition of demand within these scales. 
Upscale and Upper Midscale properties serve the broadest basket of travelers in the industry with a guest base that includes leisure travel from middle-income families, small and medium-sized corporate travelers, and an increasing share of group demand. Overall, travelers in these scales are highly sensitive to changes in overall economic activity. When economic growth accelerates and purchasing power increases, individuals are more likely to take discretionary trips, and organizations are more willing to greenlight meetings, conferences, and group events. 

Conversely, when economic conditions weaken, often accompanied by increases in the unemployment rate, these travelers are often the first to reduce trip frequency, shorten stays, or postpone events. 

Correlations are modest at the top of the market

The upper-end scales, Luxury and Upper Upscale, maintain a more moderate relationship with typical macroeconomic indicators. 

•    Luxury demand, for example, exhibits correlations of only 0.55 with GDP and 0.57 with inflation, nearly 20 to 25 basis points less than the middle scales. 
•    Upper Upscale demand produces similarly moderate relationships, with values around 0.45. 
•    While these figures remain statistically meaningful, they are substantially lower than those observed within the middle scales, and their predictive value should be met with increased scrutiny and forecast inclusion driven by additional analytical reasoning.

These reduced relationships stem from the unique demand composition of the upper-end scales. Leisure demand for higher-end properties is heavily concentrated among affluent households whose travel behavior is less impacted by changes in wages or employment. Shifts in consumer behavior are more often based on microeconomic conditions of individual travelers instead of mixed perception or unease in the general economy. On the group side, upper-end properties capture most corporate meetings, major sporting matches, and international-draw events. Considering the robustness of high-end leisure travel and the consistency of group bookings, aggregate measures such as GDP or wage growth may not fully capture the drivers of demand within these scales.

Credit utilization is a compelling alternative variable

Alternative variables identified in the analysis follow similar patterns for upper and middle chain scales, suggesting that consumer-level financial behavior might provide a stronger signal than some of the traditional macroeconomic inputs. 

•    Credit utilization demonstrates a correlation of about 0.75 with Luxury and Upper Upscale demand, significantly exceeding the relationships observed with traditional inputs. 
•    Shifting to the middle scales, credit utilization achieves a correlation in line with the strongest macroeconomic variables.

These stronger relationships may reflect the changing nature of consumer spending with travel demand increasingly dependent on overall purchasing power, compared to income levels. 

Credit utilization captures actual spending activity occurring within households and therefore may provide a more direct measure of travel participation than topline economic output and movement. 

Income levels are telling as well

Speaking of income levels, the strongest relationships are observed within the middle scales. 
•    Correlations reach 0.80 for Upscale and 0.71 for Upper Midscale demand. 
•    These mid-level properties capture the point at which households usually move between chain scales as income levels change. As households enter higher income brackets, they frequently trade up for accommodations, with the opposite occurring during periods of financial pressure. 

This substitution effect is an important component of demand allocation across the hotel industry. Rising household incomes don’t just simply increase overall travel—they also influence where that demand is captured. Moving into a higher income bracket might not affect travel frequency directly but is demonstrated by shifting lodging preference upward. On the flip side, households experiencing financial pressure may maintain travel frequency while selecting lower-cost accommodations. 

Income Band measures arguably represent these movements more effectively than traditional macroeconomic variables due to reflecting changes in the distribution of travelers across income brackets.

The negative correlations between the middle and lower-end scales visualize the trend of upward and downward shifts. As more households enter the lower income bands, demand falls in the Upscale and Upper Midscale segments as travelers adjust financial prioritizes. The same logic holds when households enter the six-figure income bands as demand preferences for lodging shift with likely easing financial pressures and more discretionary spending potential. 

Arriving at the lower-end scales, Midscale and Economy display a different trend. Bookings for these properties are often driven by necessity instead of leisure, are less exposed to corporate travel, and follow more atypical staying patterns. 

•    Considering these differences, traditional macroeconomic variables exhibit considerably weaker relationships with demand, including a GDP correlation of only 0.37 for Midscale and a negative relationship of -0.25 for Economy. 
•    These weaker results suggest that aggregate economic conditions are less useful in explaining demand behavior within the lower scales and furthermore, the negative correlation for the lowest scale could represent the trade-up effect when guests shift preferences upward as macroeconomic conditions improve. 

The demographic base of these scales differs substantially from the upper and middle tiers. Demand within Midscale and Economy properties is concentrated among value-oriented travelers whose lodging decisions are often driven by financial constraints.

•    The alternative variables identified support this conclusion with auto and credit loans exhibiting correlations of 0.57 with Midscale and 0.39 with Economy, significantly outperforming the traditional top-line variables. 
•    As well, loan delinquencies correlation also demonstrates strong statistical relationships around ~0.35, displaying values at or above typical inputs. 

These variables likely perform better because they capture the financial realities facing lower-income households. For consumers in these segments, the ability to travel is often determined less by national economic growth and more by the financial day to day of personal financial situations. A household experiencing increasing debt burden may reduce discretionary travel regardless, as well as demonstrate shifts in trip frequency or length of stay with hotel bookings.

Conclusion
All of this taken together shows the shifting landscape of usual demand drivers. The K-shaped economy has seen a bifurcation in hotel demand patterns, but a look further under the hood suggests that demand drivers are becoming increasingly segmented by individual chain scale. The traditional macroeconomic variables remain highly effective for select-service demand due to those scales serving the broad middle of the travel market. However, upper-end demand appears increasingly influenced by measures of consumer spending behavior and financial capacity. Conversely, lower-end demand appears more closely linked to household economic conditions and financial stress.

No single set of variables is likely to provide the optimal forecasting power across the full hotel industry. Scale-specific variables may offer greater explanatory power than the traditional macroeconomic inputs, and the current decoupling of the major U.S. indicators to consumer spending and perception will likely only accelerate that shift.

Source: View the original article at CoStar.