ii. Planning with Real Performance Data Instead of Proxies
How legible assets improve urban decision-making.
Overview
Urban planning decisions are made using proxies: zoning classifications establishing what uses are permitted and at what densities, average consumption figures based on building types and occupancy assumptions, modeled demand projecting future needs from demographic trends and economic forecasts, and assumed performance benchmarks derived from code requirements or industry standards. These tools are necessary when real performance data is unavailable—when cities lack visibility into how assets actually operate, consume resources, serve populations, and respond to stresses. Proxies provide defensible baselines enabling decisions to proceed despite information gaps.
However, as cities become more complex through densification and diversity of uses, more constrained by climate impacts and resource limits, and more accountable for outcomes related to equity, sustainability, and resilience, reliance on proxies increasingly produces misalignment between policy intent and real-world outcomes. A zoning classification assumes certain impacts but cannot verify them. A building type suggests performance characteristics that individual assets may not exhibit. Historical averages smooth variation that matters for targeted interventions.
Planning with real performance data replaces assumptions with evidence, allowing cities to respond to how assets actually behave rather than how regulations expect them to behave. This shift does not eliminate proxies—no city can measure everything continuously—but it transforms them from primary decision-making tools into starting points refined by empirical observation. The move from proxy-alone to proxy-informed-by-evidence represents fundamental improvement in planning precision and policy effectiveness.
Why Proxies Dominate Planning Today
Proxies persist in urban planning because they are administratively convenient, providing simple classifications that enable consistent treatment across many assets, and historically necessary when systematic performance measurement was infeasible given available technology and resources. Cities rely on zoning envelopes defining maximum buildable volumes instead of measuring actual utilization and occupancy patterns. Code compliance verification confirming that buildings meet minimum standards rather than assessing operational performance. Typological assumptions treating all industrial buildings, or all multi-family residential, as functionally equivalent. Historical averages using past consumption or traffic patterns to project future needs without accounting for recent changes.
These proxies simplify decision-making by reducing complexity to manageable categories. They provide defensible baselines grounding decisions in established precedent rather than contested measurements. They enable planning to proceed despite incomplete information about actual conditions and performance. For much of planning history, these advantages outweighed limitations because alternatives—systematic measurement of asset-level performance across entire cities—were practically impossible given available tools and institutional capacity.
The approach is not wrong in the sense of being invalid methodologically. It is incomplete because it cannot capture variation within categories, changes over time, or context-specific behavior that affects outcomes. A residential neighborhood zoned for single-family homes may contain structures with vastly different energy efficiency, stormwater management, occupancy levels, and maintenance quality—differences that matter for infrastructure capacity, climate impact, and service delivery but remain invisible in proxy-based planning.
The Limits of Proxy-Based Planning
Proxies obscure variation that matters for policy effectiveness and resource allocation. Two commercial buildings within the same zoning category, constructed to the same code requirements, located in the same district, may differ dramatically in energy performance with one consuming double the resources of the other due to system efficiency, operating practices, or occupancy patterns. Utilization patterns where one building operates at 95% occupancy while another sits 40% vacant despite identical zoning allowances. Maintenance quality where one property receives proactive upkeep while another defers work, affecting longevity, safety, and neighborhood impact. Resilience to stresses like extreme weather, with one building weathering events successfully while another requires emergency intervention.
When planning decisions treat these assets as equivalent based on proxy classifications, outcomes diverge from expectations. Infrastructure capacity planned using average consumption fails when actual use concentrates in specific areas or times. Sustainability goals based on code compliance miss the gap between minimum standards and actual performance. Resilience investments target broad categories rather than specific vulnerabilities revealed through stress testing and incident history.
Proxy-based planning smooths complexity at the cost of accuracy. This simplification made sense when measurement was expensive and data management was manual. As technology enables cost-effective monitoring and digital systems can process granular information, the trade-off shifts. The administrative convenience of proxies decreases relative to the planning value of performance data revealing actual conditions and outcomes.
How Performance Data Changes the Planning Lens
Real performance data introduces specificity enabling planners to move from theoretical assumptions to empirical observations. Instead of asking "What should assets of this type do based on their classification?" planners can ask "What are these specific assets actually doing based on measured behavior?" This shift from normative expectations to descriptive reality fundamentally changes what questions can be answered and what interventions can be designed.
Performance data encompasses measurable indicators of asset behavior including energy and water consumption trends showing actual resource use rather than modeled estimates, allowing identification of inefficiencies and validation of conservation programs. Utilization and occupancy patterns revealing how spaces are actually used versus designed capacity, informing infrastructure planning and zoning adjustments. Maintenance and downtime history documenting system failures, repair frequency, and service interruptions that affect reliability and lifecycle costs. Compliance and safety incidents recording violations, accidents, and enforcement actions indicating where policies succeed or fail.
These signals reveal how policies interact with reality in ways that proxies cannot capture. A mixed-use zoning overlay intended to create walkable neighborhoods can be evaluated not just by counting permitted uses but by measuring actual trip patterns, pedestrian activity, and local business performance. A green building code can be assessed not just by compliance rates but by actual energy performance comparing certified buildings to pre-code construction. An infrastructure investment can be justified not just by capacity models but by documented peak loads and system stress indicators.
From Static Classifications to Dynamic Understanding
Traditional planning relies heavily on static classifications that remain fixed until formal revisions occur through political processes. Land use categories define permitted activities. Building classes establish regulatory treatment. Zoning districts specify development standards. These classifications provide stability and predictability but respond slowly to changing conditions. A commercial district designated decades ago continues that designation regardless of how actual uses evolve. A residential zone maintains restrictions even if occupancy patterns change fundamentally.
Performance data enables dynamic understanding where assets are evaluated not just by their designated category but by their measured behavior. Assets can be assessed based on intensity of use rather than permitted designation—a light industrial site operating well below capacity differs materially from one maximizing allowable activity, yet both receive identical regulatory treatment under static classification. Outcomes actually achieved rather than intent expressed through zoning—a transit-oriented development zone intended to reduce car dependency can be evaluated by measuring actual mode share rather than assuming proximity produces desired behavior. Behavior demonstrated through operation rather than form prescribed by regulation—buildings designed identically may perform very differently based on management quality, tenant behavior, and maintenance practices.
This dynamic understanding allows planning to adapt as conditions change rather than waiting for periodic comprehensive plan updates or major redevelopment to trigger reassessment. Infrastructure capacity can be adjusted based on measured demand rather than waiting for political appetite to rezone. Sustainability policies can be refined based on actual performance gaps rather than assuming code compliance achieves targets. Resilience investments can shift to observed vulnerabilities rather than generic typological assumptions about risk.
Capital Allocation Improves with Performance Visibility
Public investment decisions allocate limited capital across competing needs including infrastructure upgrades replacing aging systems or expanding capacity, resilience initiatives hardening critical assets against climate impacts and operational disruptions, and sustainability programs improving efficiency and reducing environmental impact. When these decisions rely on proxies—age of infrastructure, zoning categories, typological assumptions—funds are allocated broadly based on general patterns rather than specific needs.
Performance data enables targeted allocation where investments address documented problems rather than assumed risks. Prioritization of high-impact interventions directing resources to assets demonstrably underperforming or approaching failure rather than spreading funds across categories. Identification of underperforming systems pinpointing specific infrastructure segments, building types, or service areas where interventions produce measurable improvements. Validation of policy effectiveness assessing whether past investments achieved intended outcomes before committing additional resources to similar approaches.
This targeting improves return on public investment by concentrating resources where empirical evidence demonstrates need and potential impact. Rather than upgrading water systems uniformly based on age, cities can prioritize segments showing stress through leak rates, pressure variations, and water quality incidents. Rather than applying generic energy programs across building types, cities can focus on structures demonstrating highest consumption relative to comparable peers. Rather than hardening infrastructure broadly, cities can target specific vulnerabilities revealed through stress testing and incident history.
Performance data does not eliminate political judgment about values and priorities—whether to prioritize economic development over environmental goals, or equity considerations over efficiency metrics. However, it improves the grounding of those judgments by providing evidence about trade-offs, costs, and likely outcomes rather than requiring decisions based purely on advocacy and assumption.
Performance Data Reduces Unintended Consequences
Proxy-based planning often produces second-order effects that undermine policy intent. Incentives misaligned with actual outcomes where regulations encourage behaviors that meet technical requirements without achieving substantive goals—for example, density bonuses intended to create affordable housing that produce market-rate units meeting minimum square footage thresholds but remaining unaffordable. Compliance without improvement where assets satisfy regulatory mandates without materially changing performance—buildings obtaining green certifications while consuming similar resources to uncertified peers. Investment where it is least needed because proxies direct resources to categories rather than demonstrated needs—infrastructure funding flowing to politically favored districts rather than areas showing objective stress.
Real performance data exposes these misalignments early by revealing gaps between policy intent and measured outcomes. A transportation policy intended to reduce congestion can be evaluated through traffic flow data rather than waiting for complaints and political pressure. An affordable housing program can be assessed through actual rent burdens and household displacement rather than counting units permitted. A climate action plan can be tracked through emissions measurements rather than assuming adopted policies produce projected reductions.
Policies can be refined iteratively before misalignment becomes entrenched, reducing long-term corrective costs both financially and politically. Early course corrections based on evidence cost less than major policy reversals after failures become obvious. Adjustments justified by data face less political resistance than changes driven by anecdote or advocacy. This adaptive approach to policy requires performance feedback loops that proxies cannot provide.
Why Cities Struggle to Access Performance Data
Despite clear value, performance data remains difficult for cities to obtain systematically. Fragmented ownership means most urban assets are privately controlled, with owners collecting information for internal purposes rather than public disclosure. Lack of standardized reporting prevents aggregation and comparison even when data exists, as different owners use different metrics, timeframes, and formats. Privacy and liability concerns make owners reluctant to share information that might reveal violations, expose competitive positioning, or create regulatory liability. Absence of incentives for disclosure means owners see costs from preparing and sharing data without clear benefits justifying effort.
Additional barriers include technical capacity limits where cities lack systems, staff, and expertise to manage large-scale performance datasets even when available. Integration challenges where performance data from diverse sources must be reconciled with planning systems designed around static classifications. Quality concerns when self-reported data cannot be verified and may reflect measurement errors, strategic reporting, or outright manipulation. Governance gaps where unclear frameworks for data ownership, access rights, and permissible uses prevent willing participants from sharing information productively.
As a result, cities plan with partial visibility even when relevant data exists. Building energy use may be measured by utilities but not accessible to planning departments. Traffic patterns may be tracked by private navigation services but not shared with transportation agencies. Asset conditions may be documented by property managers but not reported to municipal oversight functions. Performance data remains siloed and inaccessible despite potential planning value.
Performance Data Does Not Require Surveillance
A critical concern about performance-based planning is that it might require intrusive monitoring incompatible with privacy expectations and civil liberties. This concern is legitimate but addressable through appropriate design. Planning with real performance data does not require tracking individual behavior, identifying specific occupants, or maintaining granular records of private activities.
Effective approaches focus on aggregated metrics reporting neighborhood or district-level patterns rather than building-specific details, preserving privacy while enabling system-level planning. Anonymized reporting stripping identifiable information before sharing with planning agencies, allowing performance assessment without exposing individual asset data. Threshold-based indicators flagging only significant deviations from norms rather than routine variation, reducing data volume while highlighting conditions requiring attention.
The goal is understanding system-level performance—how districts consume resources, how infrastructure operates under stress, how policies affect aggregate outcomes—not tracking individual behavior. A city assessing transportation policy needs origin-destination patterns and mode share data, not individual trip tracking. A sustainability program requires building-type energy profiles, not apartment-level consumption monitoring. This distinction is critical for maintaining public trust while enabling evidence-based planning.
Standards enabling anonymous contribution of performance data to shared aggregates can provide planning insights without compromising privacy. Individual buildings reporting energy data to anonymized benchmarking systems allow cities to establish baselines and track trends without identifying specific properties. Aggregated traffic patterns from navigation apps inform congestion management without tracking individual routes. This approach balances legitimate planning needs against reasonable privacy expectations.
The Governance Challenge
Performance data raises governance questions that cities must address before scaling adoption. Who defines which metrics matter for planning purposes, balancing comprehensiveness against collection burden and ensuring measures align with actual policy goals? Who validates reported data to ensure accuracy and prevent gaming, establishing verification mechanisms without creating prohibitive compliance costs? How is data used in decision-making to inform rather than determine outcomes, maintaining democratic control while incorporating evidence?
Without clear governance frameworks, performance data becomes contested rather than clarifying. Stakeholders dispute metric definitions favoring measures that make their positions look better. Data quality concerns undermine confidence in findings. Applications to enforcement rather than planning create resistance to disclosure. These governance challenges require resolution through transparent processes establishing legitimate frameworks for collection, validation, and use.
Successful adoption requires shared frameworks negotiated among stakeholders rather than unilateral mandates imposing city preferences. Industry engagement in defining practical metrics that balance planning value against collection costs. Privacy safeguards establishing clear limits on data use and preventing mission creep toward enforcement. Quality standards ensuring reported data meets reliability thresholds supporting consequential decisions. Dispute resolution processes handling disagreements about interpretations and applications.
Why Proxies Will Not Disappear
Clarifying realistic expectations: proxies will remain necessary tools in urban planning indefinitely. No city can measure everything continuously across all assets, activities, and outcomes. Resource constraints, privacy considerations, and practical feasibility ensure that proxies continue serving essential functions in planning processes.
The shift is not from proxies to data as if one replaces the other completely. The shift is from proxies alone to proxies informed by evidence where empirical observations refine theoretical assumptions, validate or challenge proxy-based conclusions, and reveal variation that matters for policy design. Performance data improves proxy application rather than eliminating their use. A zoning classification remains useful starting point but gets adjusted based on measured outcomes. A typological assumption provides baseline but gets refined by asset-specific performance. A historical average offers reference but gets updated with current observations.
Performance data refines assumptions rather than replacing judgment. Planners still decide what outcomes matter, what trade-offs are acceptable, and what interventions are feasible given political and resource constraints. Evidence informs these judgments by clarifying likely consequences, revealing actual conditions, and tracking policy effects—but it does not determine answers to fundamentally political questions about community values and priorities.
Why This Guide Matters
Cities face growing pressure to deliver better outcomes with limited resources. Demands for sustainability, resilience, equity, and quality of life intensify while revenues constrain, infrastructure ages, and populations diversify. Planning based solely on proxies is increasingly misaligned with this reality. Proxy assumptions smooth complexity in ways that hide variation mattering for effective intervention. Static classifications cannot adapt to changing conditions. Typological thinking misses asset-specific performance affecting outcomes.
Incorporating real performance data allows cities to plan with greater precision targeting investments to demonstrated needs rather than assumed categories, adapt to changing conditions rather than waiting for formal regulatory updates, and align policy with observed outcomes rather than theoretical models of how regulations should work. Better planning does not start with smarter rules generating more sophisticated proxies. It starts with clearer visibility into how assets actually behave, consume resources, serve populations, and respond to stresses.
The path forward requires addressing both technical and governance challenges. Technical infrastructure enabling cost-effective performance monitoring, standardized reporting formats, and integration with planning systems. Governance frameworks establishing legitimate processes for defining metrics, validating data, and incorporating evidence into decisions while protecting privacy and maintaining democratic control. Institutional capacity building planning staff capability to work with performance data and interpret findings appropriately.
Organizations providing performance data infrastructure—whether through voluntary disclosure programs, regulatory requirements, or market-based platforms—enable cities to move beyond proxy-alone planning toward evidence-informed decision-making. This shift improves planning precision, policy effectiveness, and resource efficiency at exactly the moment when cities can least afford inefficiency and misalignment between intent and outcome.
Keywords: urban planning, performance data, city infrastructure, evidence-based planning, sustainability metrics, asset performance, public capital allocation, smart cities, data-driven policy, proxy refinement
References
Centre for Digital Built Britain. Information Management for the Built Environment. Framework for structured data supporting infrastructure planning and asset management.
OECD. Using Data to Improve Public Investment Planning. Analysis of how performance data enables targeted capital allocation and validation of infrastructure investment outcomes.
UN-Habitat. Urban Data and Evidence-Based Planning. Guidance on incorporating empirical observation into planning processes while addressing privacy and governance challenges.
World Bank. Performance-Based Infrastructure Management. Framework for using asset performance data to inform maintenance, capital investment, and service delivery decisions.
Last updated