Incorporating U.S. Consumer Confidence into Real Estate Feasibility Studies

michalmohelsky
2 hours ago
30 min read

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Understanding U.S. Consumer Confidence and Its Measurement

Defining Consumer Confidence: U.S. consumer confidence refers to the degree of optimism or pessimism that consumers feel about the overall state of the economy and their personal financial situation. It is essentially a psychological barometer of economic health, gauging whether consumers feel secure enough to spend or inclined to hold back. The most widely cited measure is the Conference Board’s Consumer Confidence Index (CCI), a monthly index benchmarked to 1985 = 100. This index is derived from the Consumer Confidence Survey, which asks a sample of 5,000 U.S. households five key questions about current economic conditions and expectations for the next six months. The survey covers consumers’ appraisal of present business conditions and employment (the Present Situation Index) and their expectations for business conditions, employment, and family income in the near future (the Expectations Index). Each response (positive, negative, or neutral) is converted into a “relative value” compared to 1985 baseline data, and these values are averaged into the overall index.

Conference Board CCI vs. Other Measures: The CCI, released on the last Tuesday of each month, is often viewed as the most credible gauge of U.S. consumer sentiment. (Another notable measure is the University of Michigan’s Consumer Sentiment Index, which is similar in concept, but our focus here remains on the Conference Board’s index.) The Conference Board survey also tracks details such as buying intentions and vacation plans, providing granular insight into consumers’ planned major purchases and travel. For example, the CCI release reports what percentage of consumers plan to take vacations or buy big-ticket items in the near term – data that can be pertinent to real estate sectors tied to travel and discretionary spending. In summary, consumer confidence measures how people feel about economic conditions now and in the future, with the premise that optimism signals higher spending and economic expansion, while pessimism signals cautious spending or even recessionary behavior. As Investopedia succinctly puts it, “if consumers are optimistic, they will spend more and stimulate the economy, but if they are pessimistic then their spending patterns could lead to an economic slowdown or recession”.

Macroeconomic and Behavioral Implications of Confidence Trends

Consumer confidence is not just a sentiment measure in isolation – it has tangible macroeconomic and behavioral consequences. Rising consumer confidence tends to correlate with greater consumer spending and aggregate demand. When households feel upbeat about their jobs and income prospects, they are more likely to loosen their purse strings. This increase in spending shifts the aggregate demand curve to the right, spurring businesses to raise production and even encouraging banks to extend more credit. In fact, periods of improving confidence often presage upticks in retail sales, automobile purchases, and even home sales as consumers feel secure enough to make major purchases. For instance, a Q&A from an economic analysis notes that increasing consumer confidence directly increases consumer spending, prompting manufacturers to boost output and even giving a lift to real estate markets as home-buying picks up. In this way, consumer sentiment can act as a leading indicator for the economy. It often peaks before an economic expansion crests and falls sharply just before or during recessions, offering early warning signals. Policymakers and investors watch these sentiment swings closely because confidence surveys are released faster than “hard” data – providing a real-time pulse. (For example, consumer confidence for a given month is reported near the end of that month, whereas official consumer spending data comes with a lag of several weeks.) High confidence, therefore, can translate into immediate economic momentum, whereas plunging confidence can foreshadow an economic slump ahead.

On the other hand, falling consumer confidence typically leads to more cautious behavior that can drag on growth. When people feel uncertain about the future – due to factors like job security fears, inflation eroding their purchasing power, or unsettling news – they tend to rein in discretionary spending and increase savings as a precaution. Big-ticket purchases (homes, cars, vacations) might be postponed until confidence rebounds. In macro terms, declining confidence often coincides with slowdowns in consumer-driven industries and can even precipitate recessions if the pullback is severe and prolonged. Historical data underscores this point: consumer confidence invariably plunges during recessions (for example, during the 2008 financial crisis and the 2020 COVID-19 shock) and then recovers as the economy improves. In October 2008, as the financial crisis deepened, the Conference Board index fell to an all-time low of 38, reflecting record pessimism and immediately preceded a sharp contraction in consumer spending. Consumers drove less, dined out less, and focused on necessities during that period. Conversely, in periods of strong economic expansion (e.g. late 2016–2017), confidence climbed to multi-year highs, and indeed U.S. consumers spent freely, boosting GDP growth. The broad takeaway is that high consumer confidence tends to fuel economic activity (a virtuous cycle of spending and growth), while low confidence can become a self-fulfilling drag as households pull back, potentially “shaping the downturn” by curbing demand.

It’s important to note that consumer confidence both reflects and influences economic realities. Some economists argue sentiment is largely a passive reflection of fundamentals like income, jobs, inflation, etc., rather than an independent driver. Indeed, confidence usually improves as hard data (employment, wage growth) improves. However, sentiment can also amplify trends: if pessimism becomes pervasive, it may cause consumers to cut spending more than underlying fundamentals alone would dictate, hence exacerbating a slowdown. Likewise, exuberant confidence can overshoot fundamentals and lead to exuberant spending or investment. For feasibility studies and forecasting, the key implication is that consumer sentiment is a leading indicator that can signal shifts in consumer behavior before they show up in sales figures. Planners should monitor these trends – a sustained drop in the Expectations Index (one component of the CCI) below ~80 is often interpreted as a warning of recession within the next year. In fact, The Conference Board itself notes that Expectations readings under 80 for multiple months have historically signaled an impending recession. Recognizing such signals allows analysts to adjust forecasts proactively (for example, by building a downside scenario with weaker demand growth if confidence is trending sharply downward).

Sector-Specific Impacts of Consumer Sentiment

Not all real estate asset classes are equally sensitive to swings in consumer confidence. In feasibility studies for consumer-driven real estate assets – such as gas stations, hotels, RV parks, and car washes – it is crucial to analyze how consumer sentiment might affect sector-specific demand drivers. Below, we explore each of these sectors in turn:

Gas Stations (Fuel Demand & Travel Behavior)

Gas stations derive revenue primarily from fuel sales and secondarily from convenience store purchases on-site. These are influenced by both essential travel (commuting, freight) and discretionary travel (road trips, vacations), as well as consumer buying habits at the pump. Consumer confidence can affect gas station performance in several ways:

Discretionary Driving and Road Travel: When confidence is high and households feel secure in their finances, people are more willing to take leisure road trips, weekend getaways, or longer vacations by car. Higher confidence often correlates with increased vehicle miles traveled for pleasure, boosting gasoline demand beyond just the baseline commuting needs. In contrast, when confidence falters, families may cancel or shorten vacation drives to save money, directly reducing fuel sales volumes. For example, during periods of economic uncertainty in 2023–2024, many Americans opted for shorter, regional trips (“micro-cations”) instead of long road voyages, leading to less driving and fuel consumption despite travel still occurring. Economic analysts observed that rising prices and low sentiment caused consumers to “pull back on discretionary travel, opting for shorter, regional trips…visible in both gas station and airport traffic data”, which signals fewer fill-ups for long trips. A feasibility study for a highway travel plaza, for instance, should account for such patterns: if consumer sentiment is expected to weaken, forecasts for tourist traffic and fuel gallons sold might need to be tempered.
Price Sensitivity and In-Store Sales: Consumer confidence also shapes price sensitivity at the pump and spending on convenience items. During optimistic times, consumers are less likely to be driven purely by gas price in choosing a station; instead they may prioritize stations with better amenities or brand reputation, and they often treat themselves to snacks, coffee, or upgrades (premium fuel, car washes, etc.) during fuel stops. Industry surveys have found that “optimistic customers may be less likely to shop solely on price and instead select a store that best fits their needs. Inside the store, sentiment affects impulse purchases – optimistic consumers are more likely to reward themselves with an on-the-go purchase”. This means when confidence is high, gas stations can enjoy higher-margin sales (inside store sales of drinks, food, etc. tend to rise) and possibly even higher premium fuel sales, supporting better overall profitability. Conversely, when consumer confidence is low, drivers become extremely price-conscious. They are more likely to shop for the cheapest gas in town, even if it means bypassing brand loyalty or extra services. Impulse buys of convenience items drop as cautious consumers stick to essentials. Indeed, in a recession scenario, analysts predicted that consumers would drive less and buy more conservatively(foregoing frivolous purchases). Feasibility studies should reflect this by modeling lower ancillary revenue per fuel customer in low-confidence or recession scenarios.
Macroeconomic Factors – Gas Prices and Sentiment: It’s worth noting there’s an interplay between fuel prices and consumer confidence as well. Sharp spikes in gasoline prices can erode consumer confidence (since fuel is a daily necessity), which in turn feeds back into decisions about driving. For example, in mid-2008, soaring gas prices above $4/gallon were cited as a key factor pushing consumer confidence to record lows, which then manifested in reduced driving demand. Conversely, falling gas prices in 2015–2016 helped improve consumer optimism, as households felt some relief in their budgets. A gas station feasibility analysis might use consumer sentiment as a demand modifier, adjusting projected fuel volumes upward in periods of high confidence (when people drive more willingly) and downward when confidence (or related economic health) deteriorates.

Historical Example – 2009 vs. 2017: In early 2009, with the Consumer Confidence Index at an historic low of 37.7, U.S. gasoline demand slackened notably. Households curtailed leisure trips, and total miles driven fell, while those who did purchase fuel often traded down to cheaper gas and minimized convenience store spending. By contrast, in late 2017 when consumer sentiment was riding high, many convenience store operators noted robust sales – not only were drivers benefiting from relatively low gas prices, but their optimism led to strong impulse purchases on-site, with industry surveys showing record levels of consumers feeling “good” about the economy. This comparison illustrates how dramatically sentiment can swing behavior at gas stations, reinforcing the need to bake sentiment assumptions into revenue projections.

Hotels (Discretionary Travel & Business Activity)

The hotel industry is highly cyclical and extremely sensitive to both macroeconomic conditions and consumer/business confidence. Hotel demand stems from two broad segments: leisure travel (vacations, personal travel) and business travel(corporate trips, conferences), both of which have discretionary elements influenced by confidence. Here’s how consumer sentiment plays into hotel feasibility:

Leisure Travel Demand: Consumer confidence is a bellwether for discretionary leisure travel. When confidence rises, households are more likely to spend on vacations, weekend hotel getaways, or visiting family – all of which boost hotel occupancy rates. A confident consumer is willing to allocate more of their budget to non-essential travel and lodging. For example, after the recession of the early 2010s, as confidence steadily improved, U.S. leisure travel picked up significantly and hotel occupancies climbed year by year. Conversely, in times of faltering confidence, leisure travel is one of the first things families cut. Vacations might be shortened, downgraded, or canceled entirely when people feel economically insecure. A current example (2025) highlights this: consumer sentiment has been falling over the past year, and the hotel industry is seeing the effects primarily in the lower-priced segments that cater to budget-conscious travelers. An industry report noted that consumer sentiment fell from an index level of 102 to 97, and hotel demand is increasingly consumer-reliant – meaning that if consumers are less optimistic, leisure-driven hotel stays soften. Indeed, data from mid-2025 showed occupancy declines concentrated in lower-tier hotels, which serve price-sensitive domestic tourists; analysts attributed this to “lingering inflation and near record-low consumer confidence tightening discretionary budgets” for average households. In contrast, higher-end hotels (catering to affluent travelers) were less affected at that time, as wealthier consumers were still spending. A feasibility study for a hotel should thus tie its occupancy and average daily rate (ADR) assumptions to consumer confidence scenarios. In high-confidence scenarios, assume healthier occupancy growth and perhaps stronger pricing power (ADR growth), especially for leisure destinations. In a low-confidence scenario, model a dip in occupancy, slower booking paces, and potential rate discounting to attract wary travelers.
Business Travel and Broader Economic Confidence: While corporate travel is influenced by business confidence and corporate profits, consumer confidence can be seen as a parallel indicator of general economic health which often moves in tandem with business sentiment. When consumer confidence is low due to economic uncertainty, businesses themselves often become cautious – trimming travel budgets, postponing conferences, or cancelling optional trips. For instance, during the 2020 COVID-19 pandemic (when confidence indices collapsed amid extreme uncertainty), both leisure and business travel virtually ceased, causing U.S. hotel occupancy to plummet over 30% and revenue per room to drop almost 50% year-over-year. Even outside of such extreme events, a mild downturn in consumer sentiment can signal softer economic growth, prompting companies to limit travel expenses. Hotels in urban centers and conference markets feel this via reduced weekday occupancies and lower group bookings. Feasibility studies for hotels that rely on business travelers (e.g. downtown conference hotels) should therefore also monitor overall confidence and include contingency plans: if consumer and business sentiment indices are trending sharply down (signaling a possible recession), one might reduce projected occupancy or increase the ramp-up period for a new hotel to reach stabilized performance. Lenders often request sensitivity analysis on hotel projections, such as What if a recession similar to 2008 or 2020 hits in Year 2 of operation? Using consumer confidence as a trigger for such a scenario is a sound approach, since historically the CCI nosedives ahead of or during lodging downturns. For example, prior to the Great Recession, the CCI fell by more than half (from the 90s in 2007 to the 30s by early 2009) and U.S. hotel occupancy similarly dropped from ~63% to ~55%, with economy hotels benefiting slightly as people “traded down” from costlier vacations. In feasibility modeling, one could correlate a 10-point drop in the CCI with a certain percentage decline in RevPAR (revenue per available room) based on past elasticities.
Segment and Regional Nuances: It’s also important to consider which hotel segment and market is being analyzed. As hinted above, high-end vs. budget hotels may see opposite effects in some downturns. In recessions, luxury hotels often suffer larger percentage revenue declines (as affluent international travelers and business conferences vanish), whereas economy hotels sometimes hold steady or even gain occupancy as some travelers downgrade to cheaper accommodations. For instance, during the 2008–2009 recession, many campgrounds and budget motels saw steady usage (people still took road trips, but opted for camping or budget lodging), whereas upscale urban hotels saw double-digit RevPAR drops. In the pandemic-related recession of 2020, drive-to destinations and interstate hotels (serving essential workers and road travelers) recovered faster, while big-city full-service hotels lagged until confidence in health safety was restored. A robust feasibility study will discuss how consumer confidence impacts the specific sub-market: e.g., a new resort hotel in a vacation area might use consumer sentiment as a proxy for expected vacation travel demand, whereas a highway motel might be more resilient but still not immune to widespread pessimism. Citing historical analogs (like 2001, 2008, 2020 downturns and recoveries) strengthens the analysis. For instance, you might note: “In the event of a confidence shock back to recessionary levels, we anticipate occupancy at the proposed hotel could fall by 10–15% (similar to the 2008–09 decline when the CCI hit record lows), and recovery may take 2–3 years post-recession.” Including such context backed by confidence index movements and hotel KPIs from those periods gives lenders and investors concrete frames of reference.

RV Parks and Campgrounds (Budget Leisure Travel & Seasonal Usage)

Recreational vehicle (RV) parks and campgrounds occupy a unique niche in the travel industry, often considered a budget-friendly alternative to traditional lodging. One might assume that RV parks, being leisure-oriented, would suffer when consumer confidence is low – and indeed, if people are truly afraid to spend, they might not travel at all. However, historical evidence suggests a counterintuitive resilience in RV park demand during economic downturns. When consumer confidence drops and households tighten their belts, many forego expensive fly-away vacations or pricey resorts, but they may turn to camping and RVing as a more affordable way to holiday. In other words, camping can be a substitute good in recessions: it delivers leisure and family time at a fraction of the cost of, say, a Disney trip or an international cruise.

Recession Behavior – “Camping is Recession-Proof?”: Industry analyses and past data indicate that campground occupancy often holds steady or even increases during recessions. For example, during the Great Recession of 2008–2009 (when U.S. consumer confidence was extremely low), many RV parks reported that reservation levels were as high as or higher than the prior year. The National Association of RV Parks and Campgrounds noted that campground occupancies and revenues in 2009 kept pace with 2008, despite the broader recession – an outcome “atypical of other travel sectors”. The reason is that millions of Americans already own RVs, and if they can’t justify an expensive trip, they will still use their RVs for nearer, cheaper vacations rather than not vacation at all. As one camping industry article put it, “when the going gets tough, people head to the great outdoors”, highlighting that outdoor recreation is a lower-cost escape and can provide a sense of normalcy in tough times. Feasibility studies for RV park developments should thus not automatically assume a worst-case occupancy collapse in a recession. In fact, a downturn might preserve or boost demand if families switch to RV trips. The 1980s, early 1990s, and 2008 recessions all saw upticks in campground reservations even as overall travel spending fell. This historical trend could be a selling point to lenders: the asset class may have defensive qualities. However, note a caveat: while park usage stays strong, RV sales (new vehicle purchases) tend to plummet in recessions, since buying a new RV is a large discretionary expense. But for park owners, the pertinent insight is that existing RV owners keep traveling and filling campsites.
Consumer Confidence and Seasonal Demand: Consumer confidence can also impact when and how people use RV parks. In high-confidence environments, not only do more people go camping, they might also travel farther or try new destinations (benefiting a broader range of parks). They are also more likely to indulge in paid campground amenities (renting that nicer RV site with full hookups, or spending on activities at the campground). In low-confidence times, campers may stick to closer-to-home trips (to save fuel) and shorter stays, but they will still camp. Seasonal usage patterns (summer vacation season, holiday weekends) are fairly robust, though the length of tripsor advance bookings might shrink if people feel financially insecure. For example, an RV park feasibility might incorporate sensitivity in average stay length: if confidence is low, perhaps assume the average stay is 3 nights instead of 4, as travelers opt for a shorter vacation. Additionally, during economic booms (high confidence), RV parks could see an influx of new RV owners and higher occupancy even in shoulder seasons. During busts, they might see more long-term stays (some individuals even live in RVs if housing is unaffordable or if they are traveling for work), which can keep occupancy surprisingly solid. One recent twist was observed in 2020: with pandemic fears high (and traditional travel confidence low), RV camping became one of the most preferred travel options because it allowed social distancing. A May 2020 survey found that RV camping led all travel categories in consumer interest and “confidence” as an appealing vacation choice, with many first-time buyers entering the market. This was a special case where health safety sentiment overlapped with consumer confidence, but it reinforced how RV parks can capture demand even when other travel falters.

In summary, when incorporating consumer confidence into an RV park feasibility study, do not assume low confidence equals low occupancy – often it’s the opposite relative to other hospitality assets. Use historical parallels: e.g., “During the 2008–09 recession, consumer confidence hit record lows, yet our industry research shows campground reservations actually increased, as families sought low-cost vacation alternatives. Therefore, in our downside scenario we project flat rather than declining occupancy for the proposed RV park, reflecting this potential counter-cyclical demand.” By contrast, in very high confidence periods, you might project strong growth but temper it by considering that some RV customers could opt for upscale travel instead. Overall, factoring sentiment here means recognizing the value proposition of camping in tough times and adjusting assumptions about occupancy resilience and revenue per guest (perhaps ancillary revenues per camper might be lower if they’re budget-conscious, even if occupancy is high).

Car Washes (Non-Essential Services and “Affordable Luxury”)

Car washes, especially stand-alone automatic car wash businesses, are often seen as a somewhat discretionary service – after all, one can always wash a car at home or postpone a wash to save money. However, the car wash industry has exhibited a degree of resilience in the face of economic swings, leading some investors to label it a “recession-resistant” or at least low-cost luxury sector. Incorporating consumer sentiment into a car wash feasibility study involves understanding this dual nature: a car wash is a small indulgence that many consumers will continue to afford even if they cut back on bigger expenses.

Consumer Spending Patterns on Car Care: During times of rising confidence and prosperity, car wash usage tends to be robust. Confident consumers might purchase more frequent washes, higher-tier wash packages, or add-ons (wax, detailing) because they feel they can splurge a little on keeping their vehicle shiny. Moreover, the trend toward convenience and subscription models (monthly wash clubs) has made car wash revenue more stable in good times and bad. When sentiment is high, car wash memberships often grow quickly as people don’t hesitate to sign up for unlimited washes monthly. The feasibility implications are straightforward: optimistic consumers support strong volume and allow for price increases over time without much churn.
Downturns and Resilience: When consumer confidence falls and budgets tighten, one might expect car wash visits to be an easy line item to cut. Indeed, operators do see some customers reduce frequency or cancel memberships if they face financial stress. However, evidence from past recessions (e.g., 2008–2009) shows only modest declines in car wash demand, with relatively quick recovery. During the 2008 financial crisis, the industry’s revenue dip was limited (a few percentage points) and it rebounded faster than many other retail sectors. One reason is that a car wash is a relatively cheap service (often $5–$15 basic wash), and many consumers still prioritize basic car care or the small morale boost of a clean car even when cutting back elsewhere. A senior VP at a car wash advisory firm noted that while consumers might pull back on some spending, it’s uncertain “whether consumers will consider carwashing expendable and reduce frequency,” since the industry historically showed durability with only modest declines in the 2008 meltdown. In fact, some describe car washes as an “affordable everyday service” or affordable indulgence that holds firm during recessions. A marketing piece for investors highlighted that people continue to wash their cars regardless of market conditions, considering it a low-cost necessity rather than a luxury (e.g. washing off winter road salt or spring pollen). While that may be a slight overstatement, there is truth that many drivers view a clean car as part of basic upkeep. Psychologically, during tough times consumers might forgo big luxuries but allow themselves small treats (“lipstick effect” analogy) – a quick car wash can be one of those feel-good small treats that survive budget cuts.
Operational Factors – Memberships and Weather: Modern car wash businesses increasingly rely on monthly membership models (unlimited wash clubs), which provide recurring revenue and can buffer against short-term dips in consumer confidence. Even if a consumer is feeling uncertain, if they’ve already subscribed, they may continue using the service since the marginal cost is zero – or they might forget to cancel, maintaining revenue. In feasibility analysis, one can argue that a strong membership base makes the business less sensitive to minor sentiment fluctuations. Of course, a severe recession could lead to cancellations, so scenario testing is needed: e.g., assume X% of members drop out if confidence falls below a certain level for an extended period. Another factor independent of confidence is weather (e.g., frequent rain can reduce demand temporarily), which often has a bigger short-run impact on car washes than macroeconomics. However, prolonged economic downturns will eventually show up in fewer impulse washes and possibly downgrades (customers opting for the basic wash instead of the deluxe). A feasibility study can incorporate a sensitivity where revenues drop perhaps 5–10% under recessionary sentiment, based on the 2008 precedent – a much smaller decline than, say, restaurants or apparel retail saw in the same period.

Bottom line for car washes: They are relatively insulated from severe consumer confidence swings, but not completely immune. Lenders and investors will appreciate seeing this nuance. For example, you might write: “Even in the 2008 recession when consumer confidence hit multi-decade lows, car wash industry sales only dipped modestly and recovered faster than other sectors. Consumers still prioritize basic car care; a car wash is viewed as a justifiable routine expense even when discretionary spending shrinks. Thus, in our downside case we assume only a minor revenue contraction for the proposed car wash.” That said, it’s wise to also mention steps the operator can take to retain customers (loyalty programs, emphasizing value) if confidence weakens – as the cited article notes, giving customers “every reason to keep their membership” is key when wallets tighten.

Lender and Investor Considerations: Sentiment in Financial Modeling and Risk Assessment

Why Lenders Care: Both lenders (banks, commercial mortgage providers) and equity investors closely watch consumer confidence as part of their macro risk assessment. While a feasibility study often focuses on project-specific metrics, savvy underwriters will question the broader economic context – “Are we heading into a favorable environment or a downturn?”. Consumer confidence is one of the leading indicators they consider to answer that. If the project’s success depends on consumer spending (which is the case for gas stations, hotels, RV parks, and car washes), then low or falling consumer sentiment is a yellow flag that demand might underperform. Lenders incorporate this in several ways:

Scenario and Sensitivity Analysis: It is common for lenders to request stress tests on pro forma financials. They might ask, “What if revenues are 10% lower than your base case? What if a recession hits in year 1 or 2?” One rational way to construct such scenarios is to tie them to consumer confidence outcomes. For example, a feasibility study could present a base case assuming consumer confidence remains in a normal range (CCI say 90–100) and an adverse case where the CCI falls below 80 (signaling a mild recession) within the next year, leading to X% drop in first-year revenue and a slower growth trajectory thereafter. Lenders appreciate when these scenarios are not arbitrary but linked to real indicators. If recent confidence trends are negative – say the index has declined several months in a row – the study might emphasize the downside scenario and show that the project can still service debt under those conditions, thereby mitigating lender concerns. On the flip side, if confidence is high but perhaps unsustainably so, a prudent analysis will stress test a return to the long-term average sentiment (or worse) to ensure the deal isn’t premised on peak optimism.
Go/No-Go Timing Decisions: Investors use consumer sentiment data when deciding when to green-light projects or acquisitions. If confidence is collapsing and other leading data suggest a recession is imminent, a lender or investor might delay funding a new hospitality project, for instance, until conditions stabilize. They remember that declines in sentiment often precede drops in revenue for these assets. Conversely, if confidence has been down but is on the upswing, it could be a good time to invest ahead of a recovery. Lenders might also incorporate sentiment into loan terms – in uncertain times (low confidence), they may require more conservative leverage, higher interest spreads, or stronger covenants to hedge against the greater risk of revenue shortfalls. Indeed, during periods of weak consumer confidence and high uncertainty (such as mid-2023, when inflation had eroded sentiment), banks broadly tightened lending standards and increased risk premiums on loans. Fitch Ratings noted in 2023 that with sentiment weakened and confidence low, lenders were imposing stricter standards, which in itself contributes to spending slowdowns. This implies for a feasibility study: if you’re presenting during a low-confidence period, you should acknowledge the likely tougher financing environment and perhaps show more robust debt service coverage or additional equity to reassure lenders.
Risk Assessment and Ratios: Lenders and investors often build macro assumptions into their cash flow models. For instance, an investor might use the Conference Board’s forecast (if available) for consumer confidence as a guide to revenue growth assumptions. If the Conference Board or others expect confidence to deteriorate in the next 6 months (perhaps due to policy uncertainty or external shocks), a lender might haircut the applicant’s revenue projections for that period. They effectively translate sentiment into expected consumer spending patterns, which then influence occupancy rates, foot traffic, or sales per customer. By referencing external data – e.g., “The Conference Board expects confidence to remain below 80 (recessionary) through next year, so we are underwriting the hotel at a 5% lower RevPAR than the feasibility study’s base case” – lenders justify more conservative loan sizing. They may also watch sentiment as the project progresses: a sustained plunge in consumer confidence while a development is under construction could prompt re-evaluation of the loan or require updated feasibility inputs.
Investor Sentiment vs. Consumer Sentiment: It’s worth distinguishing that investors themselves have sentiment cycles (bullish or bearish) which affect capital availability. Often, consumer sentiment and investor sentiment move together – a booming economy lifts both. But there are times they diverge. Feasibility reports sometimes include a section on “market sentiment” for investors (are investors bullish on the hotel sector this year or not?). While not the focus here, be aware that if consumer confidence is low, chances are investors will be more risk-averse too, affecting valuations and required returns. For example, in a low-confidence environment, an equity investor might demand a higher cap rate (lower price) for a hotel because they fear weaker cash flows, which is effectively incorporating consumer sentiment into the investment decision.

In practice, incorporating sentiment data: A professional feasibility report might include recent charts or figures of the Consumer Confidence Index and discuss their implications. For instance, “As of the report date, the Consumer Confidence Index stands at 89.1, down from 110 earlier in the year, reflecting a significant decline in consumer optimism. This downward trend suggests increased caution in consumer spending in the coming months. Accordingly, our projections for the gas station’s convenience store sales and the hotel’s occupancy in Year 1 have been adjusted downward by 5-10% to account for softer demand.” By explicitly making this connection, the feasibility study shows lenders that the analysts are grounding their assumptions in real-world sentiment indicators, thereby strengthening the credibility of the analysis.

Additionally, lenders sometimes incorporate consumer sentiment into risk rating models or early warning systems for existing loans. For example, if a bank has many hotel loans, and consumer confidence plunges, they might flag that portfolio for closer monitoring, anticipating stress. The Conference Board’s Expectations Index under 80 (as mentioned earlier) might prompt banks to prepare for a recession scenario in their portfolio stress tests. Therefore, when presenting a new project, it’s wise to demonstrate awareness of these macro signals. Showing that the project can achieve acceptable debt service coverage even if consumer sentiment (and thus spending) weakens provides comfort. On the equity side, investors will look at IRR sensitivity: what if the first two years’ cash flows are lower due to an economic dip? Using consumer confidence-driven demand adjustments can answer that.

In summary, lenders and investors use consumer sentiment as a gauge of revenue risk. Feasibility studies should not treat macro sentiment as an afterthought; instead, integrate it by presenting multiple cases, discussing how sentiment trends have historically impacted the subject asset type, and articulating the risk mitigation strategies (e.g., cost controls, marketing offers) if a confidence-driven slowdown occurs.

Recommendations: Integrating Consumer Confidence into Feasibility Study Assumptions

To make a feasibility study robust and dynamic, especially for consumer-dependent real estate assets, it’s advisable to explicitly factor consumer confidence metrics into your forecasting and risk analysis. Here are some concrete recommendations for doing so:

Use Consumer Confidence in Demand Forecasting: Incorporate the Consumer Confidence Index (and/or sub-indices like Present Situation and Expectations) as a variable or reference point in your demand models. For example, if you have a regression or model for hotel room night demand or gas station gallons sold, consider including lagged CCI as an independent variable to project future demand. If formal modeling isn’t possible, at least use the latest confidence levels to qualitatively adjust your forecasts. For instance, “Given the current Confidence Index is ~90 (slightly below the long-term average of ~100), we forecast hotel occupancy at 70% rather than the 75% seen in the last expansion, aligning with slightly subdued consumer enthusiasm.” As the project moves forward, update these assumptions with real data – dynamic feasibility means revisiting your projections as new monthly confidence data comes out. If the CCI suddenly drops 10 points due to an event, revise the near-term outlook accordingly and inform stakeholders.
Develop Scenario Matrices Tied to Confidence Ranges: Create scenarios such as Optimistic Case, Base Case, and Pessimistic Case, explicitly linked to consumer confidence outcomes. For example: an optimistic scenario could assume the CCI rises above 110 (indicating strong growth mood) which might yield, say, 5% higher revenues across your asset (more trips, higher spending per visit). The pessimistic scenario could assume the CCI falls below 80 (signaling recession risk), leading to a 10–15% decline in discretionary revenue streams. Lenders will often gravitate to the pessimistic case for underwriting, so having it prepared demonstrates diligence. Make sure each scenario’s rationale is explained: “In the Low Confidence Scenario (CCI < 80), we assume vacation travel is curtailed significantly (hotel occupancy 10 pts lower) and local consumers cut back on non-essentials (car wash tickets -15%). These assumptions align with observed drops during the 2008 recession when the CCI hit the 30s.”
Incorporate Sensitivity Testing (Tornado Charts or Tables): Perform sensitivity analysis on key outputs (NPV, IRR, DSCR) for changes in consumer-driven inputs. For instance, you might show how the project IRR changes if annual customer volume is ±5% or ±10% from the base case. Then, connect those swings to what confidence shifts would likely cause them. “A 10% drop in year-one visitors to the theme park would reduce IRR by X%; this could occur in a scenario of very low consumer confidence similar to the post-9/11 dip or early COVID lockdown period when travel sentiment was extremely depressed.” By quantifying this, stakeholders can see the margin of safety. If the project still barely meets loan covenants under a low-confidence scenario, that’s a red flag – you might then suggest mitigating measures or a contingency reserve.
Adjust Growth Rates and Ramp-up Periods Based on Sentiment: Feasibility studies often include assumptions about how quickly a new property will ramp up to stabilized performance or what long-term growth in revenue will be. These should be adjusted with an eye on consumer sentiment. If confidence is currently high but expected to revert to normal, you might temper the growth rates after the first year, assuming some normalization. If confidence is currently low (but perhaps projected by economists to recover in 2 years), you might assume a slower first year, then a sharper uptick later. Essentially, align your growth narrative with sentiment: “Due to prevailing consumer caution, we project a 5% revenue growth in Year 1 (lower than historical average), followed by 8% in Year 2 as confidence is anticipated to rebound per XYZ forecast.” This dynamic approach ensures the study isn’t static or oblivious to the economic context.
Monitor and Update – Dynamic Reporting: Particularly for multi-year development projects, set up a process to update the feasibility analysis periodically with the latest consumer confidence data and other indicators. Many feasibility reports are done at a point in time, but conditions can change. You might include a recommendation that management track the Conference Board’s index monthly and be prepared to adjust marketing or pricing strategies. For example, if confidence falls sharply, a hotel could pivot to promotions targeting local staycations (capturing those who still travel) or a gas station might emphasize loyalty programs to retain price-sensitive customers. Including a brief plan in the study on how to respond to sentiment changes adds value – it shows the project is proactively managed.
Historical Case Studies in the Report: As part of integrating consumer confidence, include mini case studies or data from prior cycles. Lenders and investors find it very useful to see, for instance, a chart of the Consumer Confidence Index over the past 20–30 years with recessions shaded, alongside metrics from the relevant industry (fuel consumption, hotel occupancy, etc.). You could reference how, during each major drop in confidence, the asset class performed. For instance: “In 2001 and 2008, consumer confidence dropped precipitously (by 40+ points). Correspondingly, U.S. hotel occupancy fell ~10% and RevPAR ~20%. Our downside model uses a 15% RevPAR decline which is in line with those historical impacts.” By tying assumptions to history, you make them more credible. If possible, cite sources for these historical impacts (industry reports, government data) to back your claims. This approach effectively bakes consumer confidence effects into the DNA of the feasibility assumptions rather than handling it as an abstract concept.
Communication of Confidence Metrics: When writing or presenting the feasibility study, communicate confidence-linked assumptions clearly. Use language like, “Assumption X is based on Y level of consumer confidence”. For example: “We assume a 5% increase in visitors in Year 3, consistent with a period of high consumer confidence (Conference Board index ~110). However, should confidence stagnate around 90, this growth could be closer to 2%.” This shows the reader that you’re not just pulling numbers out of thin air – they are contingent on identifiable external factors. It also educates the stakeholder: they will understand that if news comes out that consumer confidence fell next quarter, they can expect perhaps a softer performance and vice versa. In a lender presentation, one might include a slide on macroeconomic assumptions with a bullet on consumer sentiment (e.g., “Base case assumes consumer confidence averages 100 in 2026–2027; downside assumes it falls to 75 in 2026 before recovering by 2028”). This ties the feasibility study to well-known economic indicators, making it far more transparent and anchored.
Link to Risk Mitigation and Contingency Plans: Finally, use consumer confidence analysis to inform your risk mitigation strategies in the feasibility report. For risks identified (like a potential confidence-induced recession), outline what the project can do to adapt. For instance, “If consumer sentiment deteriorates and revenue falls 10%, the car wash can reduce operating hours on low-demand days and pause expansion plans to conserve cash. Additionally, marketing can pivot to emphasize the cost-value (unlimited washes for $20/month) to retain budget-conscious customers.” By linking these strategies to the sentiment risk, you demonstrate that the project can be resilient. Lenders will often ask, “What’s your plan if things go south economically?” Showing you’ve thought of that in the context of consumer sentiment trends (which are often the cause of things going south) is very persuasive.

In conclusion, dynamically factoring consumer confidence into feasibility studies involves using it as both a predictive tool and a stress-test parameter. It enriches the analysis by connecting micro-level projections to macro-level consumer behavior. By defining clear scenarios, regularly updating assumptions, and planning responses around consumer sentiment metrics, developers and financiers can make more informed decisions and be better prepared for whatever the economic winds bring.

Historical Perspective: Confidence Shifts and Downstream Effects

To tie everything together, it’s instructive to recap some historical shifts in U.S. consumer confidence and their real-world effects on these asset types:

Early 1990s Recession: Consumer confidence dipped sharply in the 1990–91 recession. Hotels saw reduced business travel, gas station volumes were flat to down as unemployment rose, but campgrounds reportedly maintained usage as cost-conscious travelers stayed closer to home. Car washes were a smaller industry then, but no massive shake-out occurred – people still washed cars albeit maybe at self-serve bays.
Early 2000s and Post-9/11: The September 2001 terrorist attacks sent consumer sentiment tumbling temporarily. Hotels experienced an immediate crisis (especially airlines and hotels), gas sales dropped with less travel for a time. But within a year confidence recovered; interestingly, many families chose driving vacations in 2002 (benefiting campgrounds and roadside hotels) due to lingering fear of flying – a behavioral nuance not purely economic but sentiment-driven nonetheless. Car washes likely saw minimal impact (in fact, the early 2000s saw growth as express washes became popular).
2008–2009 Global Financial Crisis: As noted, this was a benchmark low for consumer confidence (CCI hit ~38, an “extreme pessimism” zone). Hotels: U.S. hotel occupancy fell roughly 10 percentage points and revenue per available room fell almost 20% as both leisure and business travel shrank sharply. It took about 3–4 years for hotel metrics to fully recover. Gas Stations: Americans drove about 3–4% fewer miles in 2008–2009 compared to prior trends, partly due to the recession and high fuel prices, so fuel sales volume stagnated. Many consumers traded down to cheaper gasoline options and convenience store sales of premium items (like gourmet coffee, etc.) slowed – exactly what one would expect with low confidence and tighter budgets. RV Parks: As mentioned, many campgrounds held steady or saw increases in campers in 2009. KOA (Kampgrounds of America) data at the time showed strong occupancy as families sought affordable vacations. Car Washes: The car wash industry, per trade sources, saw only a small dip in 2008. A senior industry executive recalled that while there was concern consumers would cut out car washing, most viewed it as affordable; the biggest impact was some reduction in frequency (maybe someone who washed weekly went biweekly) and a short-lived drop in more expensive detailing services. By 2010, car wash revenues were climbing again along with confidence. Notably, convenience stores and gas chains that offer car washes often tout them as resilient profit centers during downturns.
2020 COVID-19 Pandemic: This unique crisis saw consumer confidence crash (the University of Michigan index hit an all-time low around 71 in April 2020, and Conference Board’s index also plunged). Importantly, this was paired with stay-at-home orders. Hotels: suffered unprecedented losses – U.S. occupancy fell to ~20% in April 2020 and annual RevPAR was down ~47%. Confidence was low because people feared both health and economy, and travel confidence only returned when vaccines and safety measures improved the outlook. Gas Stations: fuel demand dropped 30-40% in spring 2020 as commuting and travel halted. As confidence gradually improved later in 2020 into 2021 (along with the economy reopening), fuel volumes rebounded, but consumer habits shifted – more road trips actually occurred in 2021 as international travel was still restricted, benefiting domestic gas stations once confidence in health safety grew. RV Parks: saw a boom in mid-2020 through 2021 – many people who wouldn’t normally RV turned to it as a safe vacation. RV sales actually skyrocketed in late 2020 (somewhat paradoxical in a recession, but this was driven by the unique nature of the crisis). The Ipsos research cited earlier found one in four respondents were planning an RV-related trip in the next year in May 2020, a very high number. This translated to record campground bookings in many regions once lockdowns eased. Car Washes: had a mixed impact – initially many car washes closed or saw low demand during strict lockdowns (people weren’t driving, so cars stayed clean). But by late 2020 and 2021, car wash usage recovered strongly as driving resumed. The subscription model proved its worth, as washes with strong membership bases continued billing monthly fees even during low usage months, smoothing revenues. Consumer confidence was volatile in 2020: as it bounced back in 2021, car wash customer counts followed suit.

These historical examples reinforce that consumer confidence has real and measurable downstream effects on each asset class. For a professional audience, including such references not only satisfies intellectual curiosity but also provides evidence for the feasibility assumptions. It shows that the analyst didn’t just guess the impact of a recession or boom – they looked at analogous periods. Always cite credible sources or data for these shifts: for instance, referencing STR reports for hotel performance, government fuel consumption stats, or association reports for camping and car washes helps substantiate the claims.

Conclusion: In feasibility studies for gas stations, hotels, RV parks, and car washes, weaving U.S. consumer confidence into the analysis is essential for a holistic understanding of demand and risk. By defining what consumer confidence is and tracking it, explaining its macroeconomic significance, detailing the sector-by-sector sensitivities, and showing how financiers consider sentiment, we create a feasibility report that is both comprehensive and grounded in real-world dynamics. The professional tone comes from basing conclusions on data and history – for example, using the Conference Board’s index as a yardstick for optimism/pessimism and linking that to revenue assumptions (with citations like “optimistic consumers spend more, boosting sectors from travel to car sales”). The recommendations provided guide practitioners to treat consumer confidence as a live variable, updating and stress-testing their projections accordingly. Ultimately, a project that can demonstrate viability across different consumer sentiment environments will instill greater confidence in lenders and investors. By acknowledging the power of consumer psychology – quantified through indices – within our feasibility studies, we ensure that our real estate investment decisions are not only based on bricks and mortar, but also on the hearts and minds of the consumers who drive the cash flow.

Sources:

The Conference Board – Consumer Confidence Survey® technical details and latest index readings
Investopedia – Definition and implications of Consumer Confidence Index
Federal Reserve Bank of St. Louis – Explainer on consumer confidence as economic indicator
Investopedia – Impact of rising consumer confidence on aggregate demand
NACS (National Association of Convenience Stores) – Consumer sentiment and gas station shopping behavior report
Convenience Store News (Jan 2009) – Record low confidence and effects on driving and spending
CBRE Hotels Research (Sept 2025) – Hotel state-of-the-union noting consumer sentiment dip affecting hotel demand
Scotsman Guide (Aug 2025) – Hotel industry challenges with low consumer confidence hitting budget hotels
RVBusiness/OutdoorMiles (Dec 2022) – Article “Camping expected to remain strong during recession”
RV Industry Association (May 2020) – Report on RV travel leading consumer confidence in travel plans during COVID
Commercial Plus (Amplify Car Wash Advisors, 2023) – “How a recession may impact the car wash industry” (industry perspective)
HBKS Wealth Advisors (2025) – Commentary on sentiment paradox, consumer behavior, and credit conditions
Fitch Ratings (May 2023) – Statement on weak consumer confidence contributing to tighter lending standards and spending slowdown