Housing-Performance Atlas of Baltimore Row Homes: Archetype-Based Multi-Hazard Baseline of Energy, Heat, Survivability, and Durability

1. Introduction

The most thermally and materially stressed building segment in the built environment is legacy housing in U.S. cities like Baltimore. More than 70 percent of occupied housing in Baltimore was built before 1940, with row homes made of thin brick, high envelope exposure, and outdated thermal systems [1,2]. These pre-code structures undergo greater stress due to material aging, increased cooling loads, and more extreme heat driven by climate change [3,4]. Cooling degree days in the mid-Atlantic have increased by over 20 percent since 1980, and temperatures exceeding 95°F have been recorded twice [5,6]. These circumstances increase energy insecurity, indoor heat exposure, and envelope degradation in the low-income communities that rely on legacy homes to be stable [7,8,9].

Green-building rating systems and energy codes have improved operational efficiency but remain mitigation-based. LEED v4 BD+C, Passive House, and BREEAM are mainly focused on efficiency and carbon reduction with little information on thermal survivability, overheating resistance, or material life extension [10,11,12,13]. The more recent resilience-oriented systems, such as RELi v2 and WELL v2, include occupant safety and redundancy [14,15,16], but occupancy and multi-domain adaptation measures are not yet provided [17,18,19,20]. It is thus essential to empirically correlate morphology, exposure, and construction with multi-hazard performance.

Simulation-based studies have begun to fill this methodological gap. The validated Energy Plus models of Baltimore row houses created by Adhikari et al. (2025) demonstrated that retrofit performance is sensitive to roof geometry, albedo, and ventilation rate [21]. Walaa S. E. Ismaeel (2023) observed that the current certification systems do not adequately reflect durability and adaptability. Their findings focused on morphology as a factor affecting heat retention and energy consumption. [22], and Santamouris (2020) and Kolokotroni (2023) observed that micro-scale overheating is caused by local form and context rather than regional climatic conditions [23,24]. Del Málaga et al. (2023) also attributed accelerated maintenance deficits in aging envelopes to thermal aging [25]. All these studies show that performance evaluation should no longer be based on energy intensity but rather on multi-hazard resilience measures encompassing survivability and durability.

Residential buildings account for about one-third of final energy consumption and contribute roughly 20 percent of global greenhouse gas emissions [1,2,3]. Attached brick row houses are a hallmark of this predicament in Baltimore: inefficient, worn-out, and highly exposed, especially in under-resourced communities [4,5,6,7,8,9]. Conventional energy-focused retrofits do not account for adaptive functions, including maintaining safe indoor conditions during outages, resistance to overheating, and material longevity [10,11,12,13]. It is projected that the region will experience further increases in humidity and extreme heat days in the middle of the century, posing a risk to occupant safety and structural stability [16,17,18]. Consequently, a single-metric emphasis on energy efficiency cannot provide resilience [19,20].

Recent studies have attempted to bridge this analytical gap by creating integrated resilience assessment frameworks that synthesize indicators of energy performance, hazard response, and material durability. Such nascent frameworks assess both mitigation potential through operational efficiency and adaptation potential, as expressed in a building's capacity to preserve safe indoor conditions and resist thermal and material stress [21,22,23,24]. In this setting, energy consumption pertains to mitigation potential; passive survivability defines thermal autonomy through power outages; overheating metrics exceed exceedance risk during extreme weather; and durability determines service-life reduction according to ISO 15686 principles [25,26,27]. Diagnosing where shortages occur within these parameters enables identifying the initial performance failure, thereby limiting a dwelling's overall resilience [28,29]. Baseline information on current U.S. row houses is still limited. Previous simulation research in Baltimore has focused mainly on full-year Energy Use Intensity (EUI) [33,34] or on rudimentary weatherization impacts [35,36], rarely connecting energy, survivability, and material degradation within a unified diagnostic framework. The absence of such multi-domain baselines constrains city retrofit initiatives' ability to prioritize investment based on measured resilience opportunities [37,38,39].

The cities, e.g., the Green Network Plan (2018) and the Climate Action Plan (2023), support the concept of retrofit-first, which combines cool roofs, energy-saving measures, and green infrastructure [46,47]. Nonetheless, there is no consistent quantitative diagnostic that could be used to convert simulation data into policy priorities [48,49,50]. DesignBuilder (EnergyPlus) and ENVI-met are now building-energy simulation (BES) tools that can be used to evaluate in detail the interactions among energy, microclimate, and surface temperatures [51,52,53]. Multi-hazard processes integrate indoor and outdoor processes, enabling the quantitative assessment of resilience [54,55], but decision synthesis is limited [56]. Housing-Performance Atlases, which are bundles of simulated and empirical indicators, are becoming valuable tools for visualizing multi-domain deficits and prioritizing retrofits [57,58,59,60].

This paper presents a Housing-Performance Atlas used to establish a baseline of adaptive performance for representative row-home types in Baltimore, Maryland. Behavior was measured against four necessary measures, namely: Energy Use Intensity (EUI), Safe-Zone Hours during 72 hours of power outages (passive survivability), Roof > 150°F degree-hours (overheating exposure), and an ISO-based service-life index (durability). Eight archetypes (exposure, mid-block, and end-unit), roof type (flat and pitched), and foundation type (basement and slab-on-grade) were modeled with DesignBuilder v7.0 (EnergyPlus v9.6) with Baltimore weather data (BWI Station 724060) in 10 minutes (timestep). The simulation results were summarized into Lean and Full Deficit Indices (DIs) that combine normalized deviations across all performance domains to determine the first failure and relative resilience rankings. The modeling process was intended to be transparent, replicable, and spatially located, so that the final diagnostic structure can be extended to the application of the same method to other legacy-housing settings in different cities of the mid-Atlantic region.

The study’s objectives are threefold:

(1) Define the multi-hazard baseline of Baltimore's representative rowhome archetypes;

(2) Integrate domain-specific measures with composite DI indicators for comparative resilience diagnosis;

(3) Develop an interoperable simulation-based model to rank retrofit and adaptation strategies in existing urban housing.

By integrating building-physics simulation and adaptation metrics, the Housing-Performance Atlas provides a reproducible method for determining where and how existing homes are under climatic stress, with implications for engineering design and equitable policy intervention. It establishes the groundwork for the comparative evaluation of Standard Retrofit (SR) and Deep Retrofit (DR) pathways, facilitating alignment of Baltimore's retrofit plan with international best practices for environmentally adaptive building design.

2. Materials and Methods

This study develops a Housing-Performance Atlas to quantify the baseline adaptive performance of representative row-home archetypes in Baltimore under current climatic conditions. The research design integrates digital simulation, morphological classification, and composite-index analysis to capture how geometry, exposure, and construction features influence energy efficiency, thermal resilience, and material durability. The methodological process followed five interlinked stages: (1) selection of performance indicators, (2) definition of archetypes, (3) numerical modeling and boundary configuration, (4) derivation of composite deficit indices, and (5) validation and sensitivity testing, shown in Figure 1.

2.1. Performance Indicator Selection

Four quantitative variables were selected to indicate the most critical dimensions of adaptation in legacy masonry housing: Energy Use Intensity (EUI), Safe-Zone Hours (SZH) of 72-hour outages, Roof > 150°F degree-hours, and Service-Life Index (SLI) based on ISO. They both identify the physical processes that contribute to occupant safety and material durability in Baltimore's hot, humid urban environment.

• Energy Use Intensity (EUI), defined as kBtu ft⁻²·yr⁻¹, is a measure of the total building energy demand when occupied under normal conditions and is used as the baseline for operational efficiency [9,33,34]. It is the most common indicator in benchmarking standards, including LEED and Energy Star [24,41], which enables it to cross-reference with empirical data, including the U.S. DOE RECS 2020 survey [5].
• Safe-Zone Hours (SZH) are the time during which the indoor operative temperature is kept within the range of 68-86°F in the event of a simulated 72-hour power interruption. The indicator is a measure of passive survivability, the capability of the building envelope and thermal mass to keep the building environment habitable in the event of a grid failure, an ever more pressing concern in Baltimore, where high-heat events and outage incidents frequently go hand in hand [18,20,21].
• Roof > 150 °F degree-hours cumulative surface-temperature exceedance above 150 °F calculated based on hourly roof-surface outputs in EnergyPlus [34]. The 150°F temperature marks the onset of softening of the bituminous membrane and the rapid deterioration of the sealant, as confirmed by ASTM E903 and ISO 15686 exposure tests [44,45]. This variable serves as a proxy for exposure to overheating and the amplification of the microclimate at the roof level [6,43].
• Lastly, the ISO-based Service-Life Index (SLI) converts the exposure to the environment into the anticipated life of the component (years). In accordance with ISO 15686-8 [45], the index adjusts the reference service life using temperature, moisture, and UV correction factors based on simulation results [59]. It is a compromise between the adaptation and asset-management approaches with a direct relationship between envelope durability and climatic stress [4,19].

Altogether, these four indicators balance mitigation (EUI), adaptation (SZH and Overheating), and resilience (Durability), and can be measured in the same simulation environment [1,2,37,39].

2.2. Study Area Context

Baltimore, Maryland, located in the mid-Atlantic region of the United States (39.29° N, 76.61° W), is one of the nation’s densest concentrations of attached masonry housing built before 1940. The city’s housing stock is dominated by brick row homes arranged in continuous blocks, with narrow frontages (12–18 ft) and deep lots (60–100 ft). According to the Baltimore Housing Market Typology (2020) and BNIA–JFI spatial indicators [61,62], over 65% of all residential parcels in East and West Baltimore fall within the pre-World War II typology characterized by solid masonry walls, low-reflectance roofs, and limited yard setbacks. These neighborhoods, such as Broadway East, McElderry Park, and Upton, are among the most heat- and vacancy-exposed zones identified in the Baltimore Green Network Plan (2018). [63]

The city’s humid-subtropical climate (Köppen Cfa) experiences mean July air temperatures around 84 °F and frequent multi-day heat events exceeding 95 °F. Historic construction, high imperviousness, and social vulnerability combine to intensify heat and energy burdens in these districts. The present study, therefore, adopts Baltimore’s legacy row-home blocks as the analytical focus, using eight archetypes derived from the Property View and BNIA-JFI morphological datasets (shown in Figure 2) to capture the most representative forms across the eastern and western housing belts.

2.3. Archetype Definition and Parametric Matrix

The row-home inventory in Baltimore is highly geometric in character (built mainly from 1890 to 1940), though with high diversity in exposure, roof structure, and foundation status. Based on the Maryland Property View and BNIA-JFI morphology data [1,2,28], twelve original typologies were discovered in the pre-1940 housing inventory. Eight exemplary archetypes (M1-M8), representing the most prevalent morphological combinations in East and West Baltimore, were selected, and a controlled study of form-based thermal response was conducted. Each archetype had a constant conditioned floor area of 1,500 ft 2, which is comparable across both exposure and geometry categories. The mapped typologies and the chosen archetypes are shown in Figure 2, and Table 1 summarizes the defining characteristics of the archetypes.

To systematize the performance variability, the archetypes were created by a 2 x 2 x 2 factorial design, which varied three binary factors, such as exposure, roof form, and foundation type, which in combination cover more than 90% of the residential geometries in Baltimore.

• The level of lateral heat exchange is determined by exposure. Mid-block units have both sides of the party walls, conductive gain and loss are minimized, but end-units have three fully exposed facades, which add solar and wind loading by about 20-25 percent.
• The shape of the roof is a difference between traditional flat-roof masonry (bituminous or EPDM membranes) and pitched-roof retrofits (asphalt shingles). Pitched roofs have higher solar absorptance and convective heat transfer, whereas flat roofs have greater stagnation of heat and moisture.
• Vertical thermal coupling is controlled by foundation type. Mass in basement buildings helps balance temperature and humidity changes; slab-on-grade foundations, used in infill construction after 1950, react more quickly to day-night temperature changes and surface runoff.

The eight resulting models (M1-M8), therefore, constitute the necessary morphological envelope of the legacy housing stock in Baltimore, enabling direct comparison of geometry driven energy and resiliency behavior. They were all modeled with the same envelope properties, schedules, and material parameters to separate geometric effects. Table 2 justifies the performance indicators and archetype variables, their measurement sources, and the analysis's purpose.

2.4. Simulation Setup and Boundary Conditions

All simulations were conducted using DesignBuilder v7.0 with EnergyPlus v9.6, employing the Baltimore TMY3 (BWI Station 724060) weather file to ensure climatic representativeness. Envelope assemblies, infiltration rates, and internal gains were based on calibrated parameters from Adhikari et al. (2025) and the Department of Energy (DOE) residential prototypes [21,29,30], as shown in Table 3. Figure 3 shows the different modeled unit boundary conditions. Simulation timesteps were set to 10 minutes, enabling hourly aggregation for diagnostic comparison. The models assumed natural ventilation protocols during outages, constant occupancy (3 persons), and standardized lighting and equipment schedules. Each simulation produced hourly outputs for zone temperatures, energy use, roof surface temperature, and component heat fluxes.

2.5. Deficit Index (DI) Computation

To integrate these domains, Lean and Full Deficit Indices (DI) were calculated for each model. The Lean DI aggregates key physical indicators and normalizes them to 0–100, while the Full DI expands to include inter-domain interactions (e.g., overheating's impact on durability), as shown in Table 4. Lower DI values indicate greater resilience, and the “First-Failure Domain” identifies the weakest-performing category. This diagnostic metric is adapted from multi-criteria resilience scoring systems used in recent climate-adaptation research [35,36,37,38].

2.6. Sensitivity and Validation

Model robustness was tested through parametric sensitivity analysis across three envelope variables—albedo (0.25 → 0.75), infiltration (0.3 → 0.6 ACH), and thermal mass (brick density +15 %) shown in Table 5. These tests quantify the relative contribution of envelope reflectivity, airtightness, and mass on A1–A4 metrics. The baseline energy model was cross-validated against published EUI ranges for Baltimore rowhomes (45–70 kBtu/ft²·yr) [21,39] and DOE Residential Energy Consumption Survey (RECS 2023) data [40], yielding deviations of ±8 %.

3. Results

3.1. Energy Performance

The baseline energy performance of the eight archetypes ranged from 46.7 to 67.6 kBtu ft⁻² yr⁻¹, illustrating a strong dependency on exposure and height (Table 6 and Figure 4). Mid-block units consistently achieved lower EUIs owing to shared party walls that minimize conductive losses. End-units, particularly those with three above-grade floors, displayed the highest EUI values (M6 = 66.7 kBtu ft⁻² yr⁻¹; M8 = 67.55 kBtu ft⁻² yr⁻¹), reflecting elevated surface-area-to-volume ratios and higher envelope exposure. The roughly 20 kBtu ft⁻² yr⁻¹ difference between M1 and M8 aligns with national retrofit energy gaps reported by USDOE (2023) and Kolokotroni (2023) [24,40].

3.2. Passive Survivability

Safe-Zone Hours (SZH) during a 72-h summer outage revealed critical differences in survivability (Table 7 and Figure 5). Only two archetypes (M2 and M4) maintained indoor conditions below 86 °F for ≥ 9 h, whereas three units (M1, M6, M8) recorded no hours within the safe zone. Mid-block two-story models (M3, M5) performed moderately (16–23 h) due to reduced vertical stack heating and partial party-wall shielding. Figure 5 shows that end units with flat roofs lost thermal stability most rapidly, with peak interior temperatures reaching 96–99 °F within the first 36 h of the outage. These findings echo urban heat survivability patterns observed in Chicago and Philadelphia case studies (Rodrigues et al., 2019; Li et al., 2024) [31,32].

3.3. Roof Overheating Risk

Roof-temperature analysis highlighted pronounced differences between flat and pitched configurations. Roof surface temperature data were obtained from the EnergyPlus variable Surface Outside Face Temperature recorded at a 10-minute timestep (Timestep,6). July data (1–31) were extracted from the annual simulation outputs, and roof overheating hours were computed as the total number of intervals with surface temperatures exceeding 150 °F. Degree-hours were calculated as the sum of temperature exceedances above 150 °F multiplied by the 10-minute interval. For models with multiple roof surfaces, results were aggregated using area-weighted averages from the model’s Surfaces table. The corresponding IDF request is provided in the Supplementary Information. As shown in Table 8 and Figure 6, flat roofs accumulated greater solar loads, averaging 165–210 hours above 150 °F during July’s 455 daylight hours. Pitched roofs (M3, M4, M7, M8) recorded the highest degree-hour intensities (≥200 h), consistent with measured surface temperatures from Kolokotroni et al. (2023) and Santamouris (2020) [23,24]. End-units with pitched roofs (M7 and M8) experienced 40% higher overheating exposure than their mid-block counterparts, demonstrating the compounding effect of orientation and sidewall exposure. Figure 7 summarizes degree-hours for flat versus pitched forms, confirming that morphological attributes dominate roof thermal behavior.

3.4. Material Durability

The temperature-based ISO durability indices ranged from 62 % (M4) to 84 % (M5), equivalent to approximately 31–42 service years remaining (Table 9 and Figure 7). Durability loss was strongly correlated with roof-surface temperature, with a regression slope of −1.2 years·°C⁻¹ (≈−2.2 years per +10 °F), consistent with heat-induced material fatigue reported by Del Málaga et al. (2023) and Li et al. (2024) [25,41]. Flat-roof mid-block models (M5) exhibited the most extended expected service life due to reduced thermal cycling, while three-story exposures (M2, M4, M6, M8) showed accelerated aging from elevated solar and convective loads. These results confirm that thermal exposure is the dominant durability driver across archetypes, underscoring the need for high-albedo, ventilated roof retrofits to mitigate long-term material fatigue. Robust checks, including minor penalties for moisture and wind, did not alter the ranking.

3.5. Composite Diagnostics and Deficit Indices

Integration of the four domains into Lean and Full Deficit Indices revealed distinct performance hierarchies (Figure 8 and Table 10). Average Lean DI scores ranged from 9.7 (M5) to 91.5 (M8), while Full DI values ranged from 7.8 to 92.4 (Table 8). Lower DI values signify higher resilience; thus, M5 (End-Flat-2F) and M3 (Mid-Pitch-2F) emerged as the most adaptation-ready archetypes. Conversely, M8 (End-Pitch-3F) exhibited the highest deficit (Full DI ≈ 92), with A1 Energy identified as the first-failure domain. Across the set, A4 Overheating and A3 Survivability accounted for 70 % of total deficits, confirming that thermal safety and heat stress dominate Baltimore’s retrofit priorities. These patterns are consistent with urban resilience analyses in three studies: Santamouris (2020), Gopinath et al. 2024, and Zhu et al. (2025) [23,41,42].

3.6. Sensitivity Outcomes

Sensitivity tests (Table 11) demonstrated that increasing roof albedo from 0.25 to 0.75 reduced A4 degree-hours by ≈ 18 % and extended service life by ≈ 1.6 years. Lowering infiltration from 0.6 to 0.3 ACH decreased EUI by ≈ 8%, while raising thermal mass by 15% increased safe-zone hours by ≈ 5 h under outage conditions. The combined retrofit scenario (“high albedo + low infiltration + mass increase”) improved Lean DI by ≈ 12 points, confirming that envelope enhancements can partially offset morphological disadvantages. These parametric findings corroborate global evidence that simple envelope interventions yield high adaptive value relative to cost [33,35,36].

4. Discussion

The findings of the Housing-Performance Atlas confirm that legacy Baltimore row homes are operating at the edge of climatic vulnerability, where minor geometric or exposure variations can significantly alter adaptive capacity. Energy, thermal, and durability results are widely dispersed across the archetypes, indicating that efficiency metrics do not adequately reflect the housing stock's resilience. Across all eight configurations, the Energy Use Intensity ranged from 46.7 to 67.6 kBtu ft⁻²·yr⁻¹, . However, safe-zone survivability hours during a 72-h outage ranged from 0 to 23 h, and roof overheating lasted 150-210 h at 150 °F. These nonlinear discrepancies confirm the new view that adaptation performance cannot be replaced by operational energy savings in hot-humid urban climates [1,2].

The data show that morphology regulates resilience more than construction age or material type. Mid-block units have the advantage of shared party walls that reduce conductive gain and loss, and end-units have approximately 25% higher EUI and four times less survivability. Basement conditions consistently improve performance across three areas, energy, survivability, and durability by 6 to 10 percent, with thermal inertia that attenuates diurnal variations. On the other hand, height and roof pitch increase envelope exposure and radiant absorption, thereby increasing overheating and service-life loss. The worst performer, M8 (end-unit, pitched roof, 3F above-grade), has all the undesirable characteristics: maximum exposure, minimum sub-grade mass, and high thermal surface area, leading to a Full DI of 92% and energy and durability failures.

Adaptation hierarchies are also further explained by the distribution of first-failure domains. The most common primary deficit was Roof overheating, then survivability, energy, and durability. This result highlights the fact that the operative limit of habitability during extreme events is not determined by energy efficiency but by thermal safety. The practical application of retrofit sequencing would thus require interventions that address surface temperature and indoor safe-zone stability, followed by incremental energy savings. These results align with the adaptive-mitigation ladder proposed by Ascione et al. [3], in which heat resilience serves as the basis for the next stage of efficiency gains.

The material durability results also support this interdependence. The service-life index ranged from 31 to 42 years, indicating that roof life could be reduced by 10 years under high-exposure conditions. A 10°F rise in average roof-surface temperature was associated with a loss of service life of about 2.5 years, consistent with ISO 15686-8 exposure factors and empirical data on bituminous membranes [4]. These correlations underscore that the concept of durability is thermally mediated: the very processes that threaten occupants by exposing them to heat are also those that accelerate envelope decay. Therefore, there is no way to consider adaptation without accounting for asset management.

Composite Lean and Full Deficit Indices are concise measures of multi-domain resilience. The values of lean DI were 9.7-91.5, and Full DI were 7.8-92.4. The ranking M5 (best) > M3 > M7 > M2 > M1 > M4 > M6 > M8 (worst) represents a near-logarithmic deterioration in performance with increasing exposure. The extremely low DI of M5 (end-unit flat + basement) indicates the stabilizing effect of sub-grade coupling and compact geometry. The second-best competitor, M3 (mid-block pitched + basement) is a compromise of moderate energy consumption and regulated overheating of the roof. On the other hand, the tall end-units (M6, M8) reveal the compounding effect of amplified heat and the rapid degradation of materials, proving that vertical extension without simultaneous envelope upgrades undermines resilience.

The fact that thermal domains (A3 + A4) account for 70 percent of total DI variance confirms that the adaptation deficit in Baltimore is mainly heat-related. Heat gain, survivability, and degradation pathways are determined by microclimatic context and geometry, even with the exact envelope specifications. The trends align with the city-level results reported by Santamouris (2020) and Synnefa et al. (2020), where local form is more significant than material conductivity in defining the intensity of heat risk [5,6]. The Atlas maps these lessons to the building level, offering a scalable method for quantifying retrofit prioritization.

From a policy standpoint, the Atlas transforms abstract resilience concepts into actionable diagnostics. Lean/Full DI thresholds can delineate Retrofit Triage Bands:

Tier 1 (Standard Retrofit) DI < 40 → low-cost envelope and ventilation upgrades;

Tier 2 (Deep Retrofit) 40–70 → comprehensive insulation, cool-roof, and HVAC electrification;

Tier 3 (Standard Retrofit + Green Infrastructure) > 70 → combined building + green-infrastructure interventions.

This stratification aligns with the European Renovation Passport logic [7] and can be implemented in the Baltimore Climate Plan to distribute incentives based on need and potential impact.

The distribution of high-DI archetypes across space organically defines the dimensions of equity. End-unit conditions are prevalent along block edges and vacant-lot perimeters, which overlap with historically disinvested neighborhoods, including Broadway East and Upton. Therefore, the lack of thermal and durability is associated with socio-economic deprivation, which supports the trends of environmental injustice reported in U.S. legacy cities [8]. The Atlas thus goes beyond technical assessment to provide a diagnosis of spatial resilience inequity, which equips municipalities with a data-driven instrument for equitable adaptation investment.

Implications of retrofit design engineering are also clear. A 30-35% reduction in degree-hours and 8–10-year service life increases with increasing roof solar reflectance (0.35-0.70) and halved infiltration (AH = 1.0 - 0.5) to lower EUI and safe-zone hours by 10 and 4 percent, respectively. These relationships determine the roof's reflectivity, airtightness, and controlled ventilation as the first-order, cost-effective resilience gains.

The Atlas methodologically confirms that high-resolution simulation is a plausible proxy for empirical diagnostics when field data are scarce. Model robustness is ensured by calibration to metered energy use (+/- 8 %) and by consistency between EnergyPlus temperature patterns. However, its limitations also become apparent in the analysis. The external factors to the model are occupant behavior, moisture transport, long-term degradation kinetics, and subsequent stages shall incorporate sensor-based validation and cost-carbon coupling to increase its predictive fidelity.

In general, the Atlas supports urban-housing adaptation as an objective engineering approach rather than a qualitative policy goal. It brings building physics into the decision-making process and equity planning by capturing the interplay among energy, heat, survivability, and durability through a single diagnostic lens. The resulting hierarchy, in which thermal safety overrides efficiency, indicates a fundamental shift like retrofits in legacy cities: not lower kilowatt-hours but safer degrees Fahrenheit. This diagnostic can be incorporated into municipal programs to help Baltimore shift its energy-focused compliance toward truly climate-responsive, equitable housing performance.

5. Conclusions

This study established a Housing-Performance Atlas for Baltimore’s legacy row homes, providing a multi-domain diagnostic that integrates energy, survivability, overheating, and durability under present-day climate conditions. Using DesignBuilder v7.0 (EnergyPlus v9.6) simulations and Baltimore TMY3 boundary data, eight archetypes were evaluated through the A1–A5 + C adaptation framework. Results reveal that form, exposure, and ground coupling govern resilience more strongly than material specification or construction age. Energy Use Intensity ranged from 46.7 to 67.6 kBtu ft⁻² yr⁻¹, yet thermal survivability ranged from 0 to 23 h and roof overheating persisted 150–210 h above 150 °F, confirming the weak correlation between efficiency and safety.

Composite Lean and Full Deficit Indices (DI = 7.8–92.4) captured resilience gradients across archetypes. The most resilient configuration, M5 (end-unit flat + basement), achieved the lowest DI (≈ 8), while the least resilient, M8 (end-unit pitched + 3F above-grade), recorded DI ≈ 92. Thermal domains accounted for roughly 70 % of total deficits, establishing overheating and survivability as the dominant failure modes in Baltimore’s aging housing stock. These findings redefine retrofit sequencing: thermal-safety stabilization and roof-temperature control must precede energy-efficiency upgrades. The Atlas, therefore, functions as both a research tool and a decision-support mechanism, enabling municipalities to prioritize limited retrofit funding toward geometries and neighborhoods where adaptation returns are greatest.

The study also advances methodological transparency by translating complex simulation outputs into a clear, reproducible index structure. The integration of service-life modeling (ISO 15686) with heat-exceedance metrics bridges building physics, asset management, and resilience policy, supporting Baltimore’s transition from energy-centric codes to climate-responsive redevelopment. Beyond its local relevance, the Atlas framework is transferable to similar mid-Atlantic cities characterized by attached masonry housing and growing urban-heat burdens.

Limitations

While the Housing-Performance Atlas establishes a reproducible baseline, several limitations remain. The simulations represent steady-occupancy and controlled-behavior conditions, excluding stochastic effects such as window operation, thermostat settings, and shading behavior, which can significantly influence survivability and overheating outcomes. Material durability estimations rely on ISO 15686 exposure factors rather than long-term empirical degradation data, potentially under- or overestimating service-life reductions under urban heat-island conditions. The study also omits stormwater and cost/carbon (+C) domains, which will be addressed in subsequent research phases to capture the full spectrum of adaptation co-benefits and to integrate standard retrofitting with green infrastructure pathways. Moreover, validation was limited to cross-comparison with benchmarked metered data; future work should incorporate in-situ temperature and humidity sensors to calibrate real-time resilience performance. Despite these constraints, the diagnostic reliability of ±8 % for energy predictions and the consistent qualitative heat-mapping patterns confirm the robustness of the framework for comparative analysis and methodological replication.