
Location utility refers to the value difference created when an asset or service is more conveniently used or traded due to its specific location. It treats “where” as a critical dimension influencing efficiency, income, and risk.
In everyday scenarios, a coffee shop near a subway entrance attracts more consistent foot traffic than an identical shop in the suburbs; a warehouse close to a highway hub reduces delivery time and costs; residential properties near established school districts are easier to rent out or sell. These location-driven advantages in usage convenience and proximity to demand exemplify location utility in action.
Price reflects the expected value of future cash flows and associated risks. Location directly impacts customer flow, logistics costs, and policy uncertainties, which in turn affect both cash flow and discount rates.
For example, shops in CBDs (Central Business Districts) often command higher “rental premiums.” Rental premium refers to the additional rent attributed to superior location compared to similar properties. This premium is driven by quality customer traffic, convenient transportation, and comprehensive amenities that boost revenue and stability. Similarly, warehouses closer to major clients can minimize “last mile” delivery costs and enhance order fulfillment speed, resulting in higher profits and valuations.
The fundamental principle of location utility lies in spatial supply-demand matching and changes in frictional costs. Frictional costs represent the combined consumption of time, money, and uncertainty—locations with better accessibility reduce frictional costs and increase operational efficiency.
When demand clusters at certain points, being close to those points enables faster access to users or clients. Where transportation, information, or services are more developed, transactions are smoother and risks more manageable—reflected in higher prices due to these efficiency advantages.
To measure location utility, the focus is on translating “accessibility, demand strength, usage cost, and risk” into observable indicators for comparison and sensitivity analysis.
Step 1: Define the intended use—whether for retail, warehousing, office space, or residential purposes—as each use has different key indicators.
Step 2: Assess accessibility by tracking subway and bus coverage, road connectivity, walking/driving times, and network quality—these reflect the ease of people or goods reaching the location.
Step 3: Estimate demand strength using customer footfall counts, local population and income data, density of competitors, and online search popularity to gauge potential demand and competitive landscape.
Step 4: Calculate returns and costs by comparing rents, sale prices, and operational expenses across similar locations. Capitalization rate (net operating income divided by asset price) can be used for horizontal evaluation.
Step 5: Conduct risk and sensitivity analysis—test how changes in transportation, policy adjustments, or consumption patterns might affect cash flows to avoid over-optimism.
In real estate, retail, and logistics investments, location utility is integral to site selection, pricing, and risk management. The process starts with defining “where are the target customers,” followed by evaluating the cost and speed of reaching them.
For instance, community retail values walkability and steady customer flow; regional warehousing prioritizes proximity to highways, ports, or distribution centers; long-term rental apartments are priced and located based on access to schools, subways, employment centers, and amenities. Quantifying these factors supports more robust project comparisons and return expectations.
In blockchain ecosystems, “location” can be interpreted as network hotspots where capital and users aggregate—for example, a specific blockchain network, a liquidity pool, or an active platform sector. The more concentrated the activity at a location, the smoother the transactions and the greater information symmetry—mirroring real-world location utility.
On-chain refers to records and interactions occurring directly on blockchain networks. Liquidity measures how easily an asset can be traded at a given spot—the better the liquidity, the faster trades settle with minimal price deviation. Slippage is the degree to which executed prices deviate from expected prices, often increasing when liquidity is lacking.
On Gate’s platform, users can observe how the same token trades at different depths and volumes across various trading periods. By choosing optimal “locations” (such as more active trading pairs or time windows), users can reduce slippage and transaction costs. When dealing with RWA (real-world assets tokenized on-chain), it is essential to evaluate a token’s on-chain “location” and platform activity level for more stable execution and price discovery.
Network effects mean that value increases as more users participate; location utility provides the initial conditions that make it easier for users to gather. A strong location first boosts accessibility and early activity; as activity grows, it attracts even more users and capital—creating a positive feedback loop.
In physical marketplaces, thriving locations attract more stores and supporting services; in online ecosystems, robust activity draws developers and capital. Both phenomena explain why success often breeds further concentration (“the strong get stronger”) but also highlight the need to monitor systemic risks from over-concentration.
Key risks include path dependency, policy shifts, data bias, and black swan events. Path dependency can lead investors to chase historically popular locations while overlooking shifts in demand. Policy changes may impact transport infrastructure, commercial zoning, or tax rates. Data bias may arise from small sample sizes or unrepresentative collection periods. Black swan events like sudden construction or public incidents can disrupt expectations.
For financial security, it is wise to set stop-losses and position limits, diversify across locations and asset classes to avoid overexposure to a single place or platform. On chain, always consider compliance requirements, smart contract audit, and platform stability—every investment carries risk of loss.
To turn location utility into actionable strategies, approach from three dimensions: objectives, data collection, and validation.
Step 1: Set clear goals and constraints—such as target returns, maximum rent levels, longest delivery times, or acceptable policy risk range.
Step 2: Establish indicator systems and data collection methods—determine metrics for accessibility, demand strength, cost, and risk with standardized collection protocols.
Step 3: Compare multiple scenarios via small-scale pilots—start with minimal investment to validate assumptions through observed foot traffic, conversion rates, and costs before scaling up.
Step 4: Employ dynamic monitoring and review—regularly update data sets, document sources of deviation, and adjust location choices and resource allocation accordingly.
On Gate’s platform, traders can incorporate “network location” into their strategy: track liquidity depth across different pairs and times for a token; optimize entry/exit points using policy updates and macro information; avoid heavy positions in illiquid “locations.”
Location utility highlights how “where” impacts efficiency, returns, and risk together. Offline, it enhances cash flow stability through accessibility, proximity to demand, and supporting amenities; online—in Web3—it manifests as where capital and users cluster within networks. Practical implementation requires measurable indicators, pilot validation, dynamic oversight—and constant vigilance regarding risks from policy shifts or demand migration. Making “location” a core factor leads to more evidence-based site selection and asset allocation decisions—and helps identify stronger liquidity and price discovery opportunities in on-chain trading.
Marginal utility is the additional satisfaction or value gained from consuming one more unit of a good or service. Put simply: every extra use brings less new happiness than the last. For example—the first bite of ice cream is delightful; the second is good too; but by the fifth bite your satisfaction drops noticeably. This illustrates the law of diminishing marginal utility.
Marginal utility is calculated by dividing the change in total utility by the change in quantity consumed: “Marginal Utility = Change in Total Utility ÷ Change in Quantity.” For example: if your total satisfaction after drinking five glasses of water is 100 points and after six glasses it’s 102 points, then the marginal utility of the sixth glass is 2 points. In practice, consumer survey data is often needed for accurate estimation.
Location utility emphasizes how geographic position affects an asset’s value—for instance, why prime real estate commands higher prices. Marginal utility focuses on how each additional unit consumed changes value. Location utility reflects value differences across “space,” while marginal utility captures decreasing value per unit along the “quantity” dimension. Both are key economic concepts but operate on different axes.
This illustrates location utility at work. Supply/demand dynamics, transportation costs, local competition—all affect a product’s value by location. For example: bottled water may cost 10 yuan at a convenience store but 30 yuan in a remote mountain area due to higher supply costs, less competition, and urgent consumer need. Understanding location utility helps optimize investment choices or pricing strategies for best outcomes.
Apply it from three angles: (1) In property investment—focus on appreciation potential driven by location rather than just size; (2) In business site selection—evaluate target customer concentration and purchasing power; (3) In asset allocation—compare “location value” differences across various markets. In short: under equal conditions, always prioritize locations or platforms with scarcity and high demand.


