Real Estate Location Analysis

Earlier this week we opined on the degree to which seasonality still exists in the apartment sector. The short answer is yes, seasonality still exists but there remains an echo effect from the pandemic. The peak (summer) months aren't garnering the same share of annual demand as history would tell us; instead, 1Q and 4Q are seeing a larger share of calendar year demand. Now, that's true at the national level. But as you might expect, market variances can be large! And with 2Q approaching its final month, we thought it would be a good exercise to look at what share of a typical year's demand happens by quarter, by market. Tons to unpack here so let's get to it. Graph shows the nation's 50 largest markets and what share of a given calendar year's worth of demand happens in a specific quarter. Then the markets are shaded darker (more seasonal) to lighter (less seasonal). If you're in Detroit or Cleveland, then you're familiar with the 2Q demand surge. In fact, more than 100% of your annual demand happens in 2Q. How can a quarter be greater than 100%? That means your other quarters (particularly 1Q/4Q) see negative absorption (or move-outs). From an applied perspective, Midwest markets see the lion's share of their annual demand figure happen in 2Q. So your leasing strategy should really be tailored to converting leads into leases during this window because if you miss the boat, then it's going to be hard to convert non-existent leads from the winter months. As you might expect, some markets buck the trend overall. West Palm Beach in fact actually sees 2Q as the weakest part of the year! Perhaps it's not so much that 2Q is 'weak' in West Palm Beach more so than it is the reverse seasonality... lots of your demand happens in the winter months due in part to snowbirds moving into the warmer weather areas during the winter. Interestingly enough, Miami is the nation's least seasonal market (at least as defined by this particular analysis). Peak quarters in 1Q/2Q see about 30% of the annual total. Meanwhile, the "off" season if you even want to call it that is 3Q which captures about 20% of the annual total. So with a +/-10% absorption swing from peak to trough, Miami is one of the few markets where seasonality actually doesn't come into play all that much. Phoenix (+/-20% quarterly swings); Raleigh/Durham and DFW (+/-25%) are two others without significant on/off seasons.

Land is dynamic. Have we oversimplified the role of land in urban planning❓ Urban planning often treats land as a fixed input- something to be zoned, serviced, and designed. But in practice, land is a dynamic market asset, and its pricing and ownership patterns can shape urban outcomes as much as the plan itself. I’ve been reflecting on this lately and identified several (what I would consider) overlooked dynamics that I feel deserve attention: ➡️Holding costs influence supply Where property taxes are low and vacancy penalties weak, owners can sit on well-located sites for years, restricting land supply even in high-demand areas. ➡️Public investment can have unintended consequences New infrastructure or amenities can inflate surrounding land values, pricing out intended beneficiaries unless value capture mechanisms are in place. This is the “displacement via land value uplift” problem. ➡️Fragmented ownership slows transformation In districts with many small parcels, assembling land for major projects can be prohibitively slow and costly. ➡️Speculation distorts development timing When prices rise on expectations rather than demand, projects may stall or pivot to higher-value uses that do not align with policy goals. While some may argue that these are “real estate” issues, they are central to shaping density patterns, affordability, and investment flows. Yet land market analysis is often siloed away from mainstream planning practice. If we want to influence the city’s future form, we must account for the incentives, externalities and constraints embedded in the land market. Sometimes this is through planning, sometimes through regulation, and sometimes through financial mechanisms. Often it involves all of the above. Without due consideration, even well-crafted policies and plans risk being overridden by underlying price dynamics. —————— I post about Urban Economics & the hidden side of cities to equip Urban Planners to make more informed decisions. Follow me for more insights. #urbaneconomy #urbanplanning #sustainableurbandevelopment #landeconomics

Most CRE investors are watching interest rates and cap rates. They’re playing the wrong game. The real market mover? Trump’s dollar policy. If his administration weakens the dollar, CRE could see major shifts. Why does the strength of the dollar matter? A weaker dollar impacts CRE in four key ways: ↳ Attracts foreign buyers, driving up prices. ↳ Interest rates could rise unpredictably. ↳ Construction and operating costs increase. ↳ Supply chains face disruption from increased costs. For decades, America championed a strong dollar to keep borrowing costs low. But Trump? He’s pushing the Mar-a-Lago Accord, a strategy to weaken the dollar, rebalance trade, and boost U.S. manufacturing. The Catch: Trump’s plan is internally inconsistent ↳ Wants a weaker dollar… but tariffs could strengthen it. ↳ Wants lower rates… but his policies could push them higher. ↳ Wants to reduce the trade deficit… but a weak dollar may not do it. There are significant implications to CRE of this dollar dance: CRE Fallout #1: Foreign buyers flood the market ↳ A weaker dollar makes U.S. real estate cheaper for global investors. * Problem: Investors caught up in rising prices without strong fundamentals. CRE Fallout #2: Inflation hammers construction & operations ↳ Higher import costs for steel, concrete, and electrical components. ↳ If inflation spikes, the Fed holds rates higher for longer. ↳ Development costs surge, making new builds tougher. CRE Fallout #3: Borrowing costs soar ↳ A weaker dollar deters foreign investors from buying U.S. Treasury bonds. ↳ Higher Treasury yields → higher commercial loan rates. ↳ If you’re not locking in financing now, you’re making a mistake. CRE Fallout #4: Trade wars disrupt supply chains ↳ Tariffs lead to retaliation, raising costs for retailers, warehouses, and industrial tenants. ↳ Global supply chain-dependent sectors take the biggest hit. What can CRE investors do? ↳ Secure long-term debt now – Today’s rates may be a bargain. ↳ Don’t chase overpriced assets – Foreign capital inflates prices, but fundamentals still matter. ↳ Lock in costs early – Developers should hedge against material price spikes. ↳ Reevaluate capital structures – Traditional leverage strategies may no longer work. Most investors ignore currency risk. This time, it could cost them. If Trump follows through with the Mar-a-Lago Accord, CRE’s rules will change overnight. Are you ready? *** Stay ahead in uncertain times. Subscribe to my free newsletter - link in my profile Adam Gower Ph.D.

Here is a curious idea: if we can somehow estimate the importance of trend/seasonal components for your data, you can use this in model building and forecasting. But how can we do this first step? Hans Levenbach PhD CPDF has an answer with his simple EDA technique. Let me explain. The core idea is simple and neat. For this example, I’ll use monthly data, like the time series in the first image. You can see that the data has strong seasonality, and we can qualitatively say that capturing that seasonal component correctly will probably solve the main problem in capturing the structure. But how can we quantify this? All you need to do is put the data in a "wide" format, with months in rows and years in columns. Then, as Hans proposed, run a two-way ANOVA with "month" and "year" to capture variability due to year (trend) and due to month (seasonality). Roughly, we take row/column means to get mean seasonal profiles and mean annual changes (trend), as in Images 3 and 4. The former has no trend, the latter has no seasonality, so they can be analysed separately. Then we calculate the sums of squares of these means from the global mean to estimate variation due to months (seasonality) and years (trend). We can also calculate the sum of squares of the irregular component (what is left), giving three elements that add up to the total sum of squares. Next step is trivial and straightforward: calculate the shares of each component in the total sum of squares. For our example, using aov() in R and then computing the total: Seasonal: 292,307,558 Trend:   176,308,365 Irregular: 33,618,630 Total:   502,234,552 So, the seasonal contribution is 292,307,558 / 502,234,552 ≈ 58.2%, the trend contribution is 35.1%, and the irregular component is 6.69%. Why bother? This simple EDA technique tells you roughly what to focus in forecasting. In this example, capturing seasonality correctly is roughly 60% of the story, with trend being second in importance. Hans goes further in his derivations, see his post: https://lnkd.in/ePsTyByg. He also analysed M3 results at some point, explaining why some methods performed better (trend dominated the data). It is worth pointing out that this approach assumes that the seasonal component does not evolve over time, which is reasonable but not always correct. Nonetheless, it is a great starting point for EDA. P.S. Hans Levenbach passed away on 7 April 2026. I wasn’t sure whether to write about it and what to write about him, but I had several nice discussions with him, and I have admired his approach to forecasting: first explore the data, then build a model. His passing is a loss for the forecasting community. P.P.S. You can read a bit about him on the IIF website: https://lnkd.in/eZbQvUrr #forecasting #datascience #stats

Recommended Reading: Supply Constraints and Housing Market Dynamics A recent working paper by Schuyler Louie, John Mondragon, and Johannes Wieland titled "Supply Constraints do not Explain House Price and Quantity Growth Across U.S. Cities" challenges conventional housing policy assumptions. The research analysed US cities between 1980-2020 and found that commonly measured "supply constraints" are quantitatively unimportant in explaining differences in housing costs and supply growth between cities. Cities with fewer constraints showed lower price growth between 2000-2020, but contrary to theory, this did not translate into higher construction output. The study reveals "missing homes" - the absence of expected higher quantity growth in less constrained cities despite lower price growth. This pattern held even during demand shocks like increased remote working (2019-2023). These findings suggest deregulation may not lead to expected increases in construction output and price reductions, challenging fundamental housing policy assumptions. Since supply constraints don't explain price differences through quantity effects, other factors drive higher prices: * Spatial equilibrium: Price differences reflecting quality of life variations * Reverse causality: Wealthier areas choosing stricter regulation * Amenity effects: Constraints directly influencing quality of life * Asset pricing effects: Taxation and risk differences between cities The findings suggest viewing housing production as flow determined by equilibrium ratios influenced by asset returns, rather than static supply curves. This shifts focus from how many homes are built to where and what type. Original paper: https://lnkd.in/eHw-dcMt Accessible explanation by Cameron Murray and Tim Helm: https://lnkd.in/eKWF45j6 This research demands that urban and housing professionals reconsider fundamental assumptions about housing markets and explore alternative explanations beyond traditional supply constraint narratives. #UrbanPlanning #HousingPolicy #UrbanEconomics #RealEstate #CityPlanning #HousingDevelopment

Seasonality is an underappreciated force shaping mobile network demand and performance across Europe. Coverage maps, population targets and median speed rankings are anchored in resident geography, but mobile load follows temporary human geography, including tourism, second homes, festivals, ski seasons, roaming and foliage patterns and weather-driven (i.e. storms) usage. Analysis of Speedtest Intelligence data across 30 European markets shows the effects of seasonality across the full network experience stack for the first time. Seasonality appears in deviations in tail (i.e. 10th percentile) download speed, upload, loaded latency, queueing and jitter. This is why annual medians can look great while networks worsen when they are needed most. Eurostat reports that July and August accounted for 31% of EU tourism nights in 2024, but 56% in Croatia, where Q3 alone represented nearly 70% of international nights. For networks, this is a temporary population shock into coastal/leisure geographies, often with weaker fixed-line infra, less Wi-Fi offload (and therefore a looser relationship between permanent population and required capacity). This tourism-driven pressure is visible in our data. In Croatia, evening peak speeds fell from 58.70 Mbps in Jan 2024 to 34.90 Mbps in Aug 2024. Congestion indicators peaked in August 2025, with median download speeds falling ~55% during the evening hours of 18:00 to 22:00 local time, while the bottom 10% of speeds dropped ~84%. The evidence points to a multi-dimensional QoE degradation (not just a simple speed decline). Loaded latency rose ~43% over the same period and jitter increased by nearly a third, which likely translated into materially weaker end-user outcomes for interactive applications such as video calling and gaming. Nordic markets (perhaps surprisingly) show a larger summer/winter swing than Mediterranean/coastal markets in relative terms in our data (partly due to the higher baseline congestion year-round in countries like Spain), consistent with reported domestic movement toward cabins, summer houses and rural leisure areas. Norway's July 2024 pattern illustrates this, with evening 10th percentile download speeds falling ~57% vs. its overnight baseline despite Norway otherwise appearing resilient in our peak-hour analysis. The inverse is also notable, with Switzerland and Austria often looking more strained in winter than summer, consistent with ski region demand and related terrain-constrained capacity planning. Seasonality is therefore not one European pattern, but a set of local demand migrations that stress different parts of the RAN & transport path. While most operators already plan around these patterns (i.e., beach towns get reinforced before summer, temporary masts/CoWs appear at festivals, and on-demand coverage is used in hard-to-serve locations), there is a policy gap in that that these interventions are treated as exceptional planning, while the end-user effect is recurring.

Continuing on the #Urbanization and #DroughtRisk theme, one of the important questions comes up is - How should we do this analysis, when many cities globally may not have the data? We address this need by developing and adopting a data-fusion framework , which is now published as 🏭 "Huang, Shuzhe, et al. "Urbanization-induced spatial and temporal patterns of local drought revealed by high-resolution fused remotely sensed datasets." #RemoteSensingofEnvironment 313 (2024): 114378." 📌 Our findings revealed that urbanization led to more intense peak drought intensity and average drought severity. In addition, urban drought fields showed lower effective radius, indicating more concentrated drought towards urban regions. 📍 From the text ....." we initially proposed a two-step fusion framework, integrating both surface (i.e., gridded data)-surface and point (i.e., in-situ data)-surface fusion. The framework was applied to generate daily precipitation and average/maximum/minimum air temperature at a 1 km resolution through the integration of high-resolution remotely sensed datasets ... 📍 "comparison of our fused data with CPC, ERA5-Land, CMFD, CHIRPS, IMERG, and TMPA products confirmed its capability in capturing local-scale meteorological dynamics by improving spatial resolution from 0.1°-0.25° to 1 km. Utilizing these high-resolution datasets, we quantified urbanization's impacts on local drought across 52 major cities ..." 📍 "We found that urbanization significantly magnified extreme Standardized Precipitation Evapotranspiration Index (SPEI) and drought severity in 69.2% and 61.5% of these cities, respectively. The effects of urbanization on extreme SPEI were amplified by the increase of urbanization rates, with a slope of −0.24 (p < 0.05). To further examine the spatial patterns of urbanization-induced local drought, we proposed a drought spatial field identification method.." #Droughts #IPCC #CityClimate Jackson School of Geosciences at The University of Texas at Austin Cockrell School of Engineering, The University of Texas at Austin University of Texas Center for Space Research #UTcityClimateCoLab

The "Just-in-Time" supply chain is dying a quiet death in small-bay warehouses across the Midwest. I spoke with a regional HVAC distributor recently. Two years ago, they held three weeks of inventory. Today? They’re pushing for three months. Trade uncertainty and tariff talk have shifted the goal from "efficiency" to "resiliency." Welcome to the "Just-in-Case" economy. When a small business adds 20% more buffer stock, they don’t need a 100,000 SF hub. They need an extra 3,000 SF flex space. The problem: that space is gone. Vacancies in the Midwest are under 5%. While Wall Street builds million-square-foot boxes for Amazon, local manufacturers and distributors are fighting over the last roll-up doors in town. This demand drives 8-9% cap rates while big-box trades at half that. The arbitrage exists because the supply is structurally broken. Are your tenants asking for expansion space earlier in their lease cycles than they were two years ago? #IndustrialRealEstate #SupplyChain #SmallBusiness #MarketIntelligence #RealEstateInvesting

Very happy to share that we've now open-sourced our high-resolution (30m) nationwide dataset on Land Use/Land Cover (LULC) and terrestrial carbon storage in Pakistan, covering the period from 1990 to 2020. This dataset accompanies our recent study published in the Environmental Impact Assessment Review journal, providing critical evidence on how urban expansion affects carbon sequestration in Pakistan. ✦ Key Insights from Our Study: ⚬ Exponential Urban Growth: Urban areas in Pakistan have expanded by approximately 1040%, resulting in substantial changes to the country’s landscape. ⚬ Reduction in Carbon Storage: This urbanization has led to a 5% decline in terrestrial carbon storage, posing challenges for climate change mitigation. ⚬ Regional Dynamics: ▸ Emerging Cities on the Rise: Cities like Rawalpindi and Peshawar experienced rapid urban sprawl, primarily converting rangelands (~47%) and agricultural areas (~35%) into urban landscapes. ▸ Afforestation Efforts: While northern afforestation projects have increased forest carbon stocks, there is a marked north-south disparity in carbon storage loss. ▸Land Use Changes: The shift from natural ecosystems to built-up areas highlights the urgent need for sustainable urban planning. ✦ Implications for Pakistan: ▸Climate Change Mitigation: This dataset is essential for understanding carbon storage dynamics, a critical component of strategies to achieve net-zero emissions. ▸Policy Development: It offers valuable insights to support sustainable land-use practices and evidence-based policy-making. ▸Research and Collaboration: Open access enables collaborative efforts among researchers, urban planners, and environmental managers, fostering data-driven environmental management solutions. By openly sharing this dataset, we aim to empower stakeholders to make informed decisions that balance urban development with environmental conservation. Open data promotes transparency, collaboration, and innovation, vital in addressing the complex challenges of climate change and rapid urbanization. 🔗Project Link: https://lnkd.in/eMDQMaHm #pakistan #lulc #landuse #landcover #urban #urbansprawl #urbanization #carbon #carbonstorage #googleearthengine #opensource #remotesensing #geospatial #gis