By Futurist Thomas Frey
Real estate is supposed to be the bedrock of wealth building—solid, tangible, and reliably appreciating. Property values are supposedly determined by market forces, objectively assessed for tax purposes, and transparently priced. Real estate professionals are supposedly fiduciaries working in their clients’ best interests.
AI analysis of real estate markets is revealing something very different: a system built on valuation fictions, information asymmetries, and practices designed to extract maximum value from buyers while concealing risks and inflating prices. The data shows that much of what we accept as “market value” is actually carefully orchestrated pricing disconnected from underlying fundamentals.
The awakening in real estate and commercial property isn’t about whether property has value—it obviously does. It’s about revealing that the systems for determining, reporting, and transacting on that value have evolved to benefit insiders at the expense of buyers, renters, and taxpayers who can’t see through the complexity.
And now AI is making them see.
The Comparable Sales Manipulation
Real estate appraisals are supposedly objective, based on “comparable sales”—what similar properties recently sold for. AI analysis reveals that appraisers systematically select comparables that justify desired valuations rather than providing objective assessments.
Here’s how it works: A home is being sold for $500,000. The appraiser needs to justify this price for the mortgage lender. They select three comparable properties that sold for $485,000, $510,000, and $520,000. Appraisal comes in at $505,000. Transaction approved.
But AI analysis of property characteristics and sales data reveals that dozens of truly comparable properties sold for $420,000-$460,000 in the same timeframe. Those comparables weren’t selected. The appraiser cherry-picked sales that justified the desired number.
AI has analyzed millions of appraisals and identified systematic bias. Appraisals for purchase transactions come in at or just above sale price approximately 92-96% of the time. This is statistically impossible if appraisers were truly objective—property values should be normally distributed around sale prices, with appraisals sometimes above and sometimes below.
Even more damning: AI analysis of refinance appraisals shows they systematically come in at exactly the value needed for the refinance to proceed. If the homeowner needs $400,000 in a cash-out refinance and has $100,000 remaining on their mortgage, the appraisal reliably comes in around $500,000—regardless of whether that represents true market value.
The pattern is clear: appraisers work backward from desired numbers rather than forward from objective analysis. And because they’re paid by lenders and real estate professionals who want transactions to close, the bias is systematic and predictable.
The Dual Agency Conflict
In many real estate transactions, a single agent represents both buyer and seller—”dual agency.” This is presented as convenient and cost-effective. AI analysis reveals it’s a massive conflict of interest that systematically disadvantages buyers.
Here’s the problem: The agent is supposed to get the best price for the seller and the best price for the buyer simultaneously—goals that are directly contradictory. In practice, AI analysis of dual agency transactions shows that buyers pay 3-7% more on average than in transactions with separate representation.
Why? The agent’s incentive is to close the deal quickly at the highest possible price—which aligns with the seller’s interest, not the buyer’s. AI has identified that dual agency transactions close approximately 40% faster than separate representation transactions and show less price negotiation. Buyers are paying more and negotiating less when they share representation with sellers.
Even more problematic: AI analysis reveals that dual agency is not randomly distributed. It’s concentrated in hot markets and with first-time buyers—exactly the situations where buyers have least leverage and most need independent representation. Experienced buyers in slower markets more often insist on separate representation. Vulnerable buyers more often accept dual agency without understanding the implications.
One comprehensive analysis estimated that dual agency costs buyers approximately $10-15 billion annually in overpayment compared to separate representation—money that flows to sellers and agents while buyers remain unaware they’re being disadvantaged.
The Property Tax Assessment Inequity
Property taxes are supposedly based on fair market value, assessed uniformly. AI analysis reveals systematic inequities that shift tax burden from commercial to residential properties and from expensive to modest homes.
Here’s what AI discovered: Commercial properties are routinely assessed at 60-80% of market value, while residential properties are assessed at 90-110% of market value. The difference represents a massive tax shift from businesses to homeowners that operates invisibly because few people compare commercial and residential assessment ratios.
Even within residential properties, AI has identified that expensive homes are systematically underassessed relative to modest homes. A $2 million home might be assessed at 85% of market value while a $200,000 home is assessed at 105%. The percentage difference seems small, but the dollar impact is enormous—and it’s regressive, shifting tax burden toward less affluent homeowners.
Why does this happen? AI analysis of assessor behavior reveals several factors. Commercial property owners hire specialized firms that aggressively appeal assessments. Wealthy homeowners do the same. Modest homeowners typically accept assessments without challenge. Assessors, facing limited resources, focus on properties that won’t generate appeals—systematically underassessing properties with sophisticated owners.
AI has also revealed geographic bias. Properties in low-income neighborhoods show higher assessment-to-value ratios than properties in wealthy neighborhoods. One analysis found that homes in the poorest quintile of neighborhoods are assessed at an average of 10-15% above market value, while homes in the wealthiest quintile are assessed at 5-10% below market value.
The cumulative effect: AI estimates that assessment inequities shift approximately $15-25 billion in annual property tax burden from commercial and high-value properties to residential and modest-value properties.
The Commercial Lease Opacity
Commercial real estate leases are complex documents running hundreds of pages. AI analysis reveals that this complexity conceals terms that systematically favor landlords while creating hidden costs for tenants.
Here’s a common pattern identified by AI: A retail tenant signs a lease with “base rent” of $30 per square foot. Seems straightforward. But the lease includes operating expense pass-throughs, common area maintenance charges, property tax escalations, and percentage rent clauses that can increase actual costs to $50-60 per square foot.
Even more troubling: AI analysis shows that landlords often control the expenses being passed through, creating incentive to inflate costs. Property management is often handled by landlord-affiliated companies charging above-market rates. Maintenance work is done by preferred vendors at premium prices. Insurance is purchased through landlord-controlled entities. All these inflated costs get passed to tenants.
AI has also revealed systematic abuse of common area maintenance (CAM) charges. Landlords charge tenants for maintaining shared spaces—parking lots, lobbies, landscaping. But AI analysis of CAM charges versus actual expenses shows that landlords often charge 130-180% of actual costs, pocketing the difference as additional profit.
One analysis estimated that opaque commercial lease terms and inflated pass-through expenses cost commercial tenants approximately $20-30 billion annually beyond what transparent, competitive pricing would generate.
The Real Estate Commission Cartel
Real estate agent commissions in the U.S. have remained remarkably stable at 5-6% of sale price for decades, despite technology dramatically reducing the work required. AI analysis reveals that this stability isn’t market-driven—it’s the result of systematic coordination that amounts to price-fixing.
Here’s the evidence: In competitive markets, technology-driven efficiency improvements reduce prices. Online research has replaced much of what agents once did. Digital listing services replaced physical showings for initial screening. Electronic signatures replaced in-person closings. Yet commissions haven’t budged.
AI analysis comparing U.S. real estate commissions to other developed countries reveals dramatic differences. The UK: 1-2%. Australia: 2-3%. The Netherlands: 1.5-2.5%. The U.S.: 5-6%. The services are comparable, the technology is identical, but U.S. commissions are 2-3 times higher.
Why? AI has identified systematic practices that maintain high commissions. Multiple listing services (MLSs) controlled by agent associations create barriers for discount brokers. “Buyer’s agent” commission splits make it financially unattractive for agents to show properties listed by discount brokers. Agents who cut commissions face social and professional pressure from other agents.
Even more specifically: AI analysis has revealed that properties listed with discount brokers (charging 1-3% instead of 5-6%) receive fewer showings, even when they’re priced competitively and in desirable locations. Agents steer buyers away from these properties because lower commissions mean lower paychecks.
One comprehensive analysis estimated that if U.S. real estate commissions aligned with international norms, home buyers and sellers would save approximately $30-50 billion annually in commission costs—money that adds no value but simply transfers from property owners to real estate professionals.
The Bidding War Orchestration
In hot real estate markets, “bidding wars” drive prices above asking. These are presented as organic market forces—multiple interested buyers competing. AI analysis reveals that many bidding wars are partially or entirely orchestrated by listing agents to inflate prices.
Here’s the strategy identified by AI: A property lists at $500,000. The listing agent knows there’s interest. Instead of accepting the first solid offer, they tell potential buyers “there are multiple offers” (sometimes true, sometimes exaggerated), creating urgency and competition. Buyers escalate their offers, bidding against each other—or sometimes against phantom competition.
AI analysis of bidding patterns shows suspicious regularities. Properties in certain markets consistently receive multiple offers within 24-48 hours of listing, regardless of season or broader market conditions. The timing is too consistent to be purely organic—it suggests that listing agents are holding properties off-market while building interest, then releasing them strategically to create artificial urgency.
Even more problematic: AI has identified cases where agents representing buyers in “competitive” situations also represent the seller or have relationships with the listing agent. These affiliated buyers submit offers that establish high price points, encouraging unaffiliated buyers to bid even higher, after which the affiliated buyers withdraw.
One analysis estimated that orchestrated bidding war tactics inflate home prices by approximately 5-12% in hot markets, costing buyers tens of billions annually while generating higher commissions for agents who create the competitive pressure.
The Commercial Property Valuation Fiction
Commercial property values are supposedly determined by rental income and capitalization rates. AI analysis reveals that valuations often bear little relationship to actual income-producing capability, especially in markets dominated by institutional investors.
Here’s the pattern: Commercial properties get valued based on “pro forma” income—what they theoretically could generate at full occupancy with optimal rents—rather than actual income. A building 60% occupied generating $3 million annually gets valued based on “potential” income of $7 million if it were 95% occupied at premium rents.
AI analysis of commercial property transactions shows that approximately 40-60% of commercial properties trade based on pro forma rather than actual income. Buyers are paying for potential that may never materialize, while sellers capture value for income they never achieved.
Even more troubling: AI has revealed that institutional investors sometimes purchase properties at inflated valuations not because they believe the pro forma, but because the purchase establishes a new “comparable” that inflates the value of other properties they own. Buy one property at an 8% premium, use it to justify 8% increases in the appraised value of ten other properties. The overpayment on one property generates paper gains on the portfolio.
This creates valuation bubbles disconnected from income fundamentals. AI analysis of commercial real estate prices versus actual rental income shows divergence that historically precedes market corrections. Properties trade based on what buyers hope they’ll be worth rather than what they currently generate.
The Property Tax Appeal Asymmetry
Property owners can appeal tax assessments they believe are too high. AI analysis reveals that this appeals process systematically advantages sophisticated property owners while disadvantaging typical homeowners.
Here’s the reality: Large commercial property owners hire specialized firms that appeal assessments annually as standard practice. These firms work on contingency—they get paid only if they reduce assessments. They’re aggressive, knowledgeable, and effective.
AI analysis shows that commercial properties that are professionally appealed see assessment reductions approximately 60-75% of the time, with reductions averaging 15-25%. Meanwhile, residential property owners who appeal on their own succeed only 20-30% of the time, with smaller reductions averaging 5-10%.
The result: the tax burden systematically shifts from sophisticated property owners to unsophisticated ones. AI estimates that the assessment appeal asymmetry shifts approximately $8-15 billion in annual property tax burden from commercial and high-value properties to residential and modest-value properties.
Even more problematic: some jurisdictions settle appeals behind closed doors with confidential agreements, preventing other property owners from learning about successful challenges and precedents that might help their own appeals. The process deliberately maintains information asymmetry.
The Rental Application Fee Extraction
Rental applications require fees supposedly covering background checks and credit reports. AI analysis reveals that landlords charge far more than these services cost, converting application screening into a profit center.
Here’s what AI discovered: A typical rental application fee is $50-100. Actual cost of a credit report and background check: $15-30. The difference—$20-70 per application—is pure profit to landlords or property managers.
In competitive rental markets, properties might receive 20-50 applications. At $75 per application times 40 applicants, that’s $3,000 in application fee revenue for services costing perhaps $800-1,200. The landlord selects one tenant; 39 applicants paid for nothing.
AI analysis estimates that rental application fees generate approximately $3-5 billion annually beyond actual screening costs—extraction from renters who often have no choice but to apply to multiple properties to secure housing.
Even worse: AI has identified that some landlords advertise properties with no intention of renting them at the stated price, collecting application fees from dozens of applicants before “deciding not to rent” or suddenly increasing the rent beyond what was advertised. The application fees become the real business model.
The Commercial Vacancy Concealment
Commercial property owners report occupancy rates to lenders, investors, and in some cases regulators. AI analysis reveals systematic overreporting that conceals vacancy problems and maintains inflated valuations.
Here’s the pattern: A commercial building is 70% occupied with paying tenants. But the landlord reports 88% occupancy by counting “lease signed but not yet occupied” spaces, short-term licenses that generate minimal revenue, and sometimes entirely phantom occupants.
AI analysis comparing reported occupancy to actual rent collection reveals systematic discrepancies. Properties reporting 85-90% occupancy often show rent collection consistent with 65-75% occupancy. The difference represents fictional occupancy used to maintain property values and loan compliance.
Why does this matter? Commercial property loans often include covenants requiring minimum occupancy rates. If occupancy falls below the threshold, loans can be called or require additional collateral. By inflating occupancy reporting, property owners avoid technical defaults while maintaining access to financing based on false representations.
AI has also identified that commercial property indexes and market reports often rely on landlord-reported data without verification, meaning that “market statistics” reflect inflated occupancy rates. This creates feedback loops—other properties get valued based on inflated market comparables.
One analysis estimated that commercial vacancy concealment inflates commercial property values by approximately 8-15%, representing hundreds of billions in overvaluation that affects financing, investment decisions, and tax assessments.
The 1031 Exchange Tax Avoidance Compounding
The 1031 exchange allows real estate investors to defer capital gains taxes by rolling proceeds into new properties. AI analysis reveals this has created a class of perpetual real estate investors who never pay taxes while accumulating massive wealth.
Here’s how it works: An investor buys a property for $500,000, holds it for 10 years, sells for $1.2 million. Instead of paying capital gains tax on the $700,000 gain, they use a 1031 exchange to buy a $1.2 million replacement property. The gain is deferred. They repeat this process indefinitely, building a portfolio worth tens of millions while never paying capital gains tax.
AI analysis of property ownership records reveals that approximately 25-35% of commercial property transactions involve 1031 exchanges. These are predominantly wealthy investors using tax deferral to compound wealth without paying taxes that other forms of investment income would require.
Even more problematic: when these investors eventually die, the properties pass to heirs with a “stepped-up basis”—meaning the heirs inherit at current market value and all the deferred gains are never taxed. The tax obligation simply evaporates.
AI estimates that 1031 exchanges defer approximately $20-30 billion in annual capital gains taxes, with much of that deferral becoming permanent through stepped-up basis at death. This isn’t tax evasion—it’s perfectly legal. But it represents a massive subsidy to real estate investors funded by other taxpayers.
The Rental Market Price Fixing
Rental prices are supposedly determined by supply and demand. AI analysis has revealed something shocking: in many markets, rental prices are effectively coordinated through algorithmic pricing software used by large landlords.
Here’s what happened: Property management companies adopted software that analyzes market data and recommends rental prices. Sounds reasonable. But when the same software is used by landlords controlling 30-60% of units in a market, and all receive similar pricing recommendations, the effect is coordinated pricing that eliminates competition.
AI analysis of rental markets where this software is widely used shows rental prices 5-15% higher than comparable markets without algorithmic coordination. The software doesn’t just reflect market conditions—it creates them by ensuring landlords don’t compete on price.
Even more specifically: AI has revealed that the pricing algorithms recommend keeping units vacant rather than reducing rents, because lower rents would affect the recommended pricing for all units in the portfolio. Better to have 5-8% vacancy at higher rents than 100% occupancy at market-clearing prices.
The Department of Justice has begun investigating some of these practices, but the scale revealed by AI analysis is staggering. Algorithmic rent coordination may be inflating rental costs by $15-25 billion annually across affected markets, extracting wealth from renters who have no idea their rents are being artificially elevated through software-enabled coordination.
The Airbnb and Short-Term Rental Impact
Short-term rental platforms like Airbnb were supposed to let homeowners earn extra income. AI analysis reveals they’ve contributed to housing shortages and price increases in many markets by removing long-term rental inventory.
By analyzing property ownership records, rental listings, and housing availability, AI has calculated that in major tourist and business destinations, 15-30% of what would traditionally be long-term rental inventory has been converted to short-term rentals. Cities like Miami, Austin, Nashville, and coastal vacation areas show particularly high conversion rates.
The effect on housing availability and prices is substantial. AI analysis shows that neighborhoods with high Airbnb concentration experience rent increases 8-15% higher than similar neighborhoods without Airbnb presence. Home prices increase similarly as investor purchases remove properties from the owner-occupied market.
Even more troubling: AI has identified that a significant percentage of short-term rentals are owned by investors operating multiple properties—not homeowners renting out spare rooms. One analysis found that approximately 30-40% of Airbnb listings are controlled by hosts with three or more properties, and roughly 10-15% are controlled by commercial operators with 10+ properties.
These aren’t people earning extra income—they’re businesses that have converted residential housing into de facto hotels, avoiding hotel regulations and taxes while removing housing from the long-term market. AI estimates this conversion has removed 800,000-1,200,000 units from long-term rental markets nationwide, contributing significantly to housing shortages and affordability problems.
The Title Insurance Racket
Title insurance supposedly protects buyers against ownership defects. AI analysis reveals it’s one of real estate’s most profitable and least justified services.
Here’s the reality: Title insurance premiums are based on property value. A $500,000 home might require $2,000-3,000 in title insurance. But the actual risk being insured is minuscule. AI analysis of title insurance claims shows that title defects requiring payment occur in fewer than 3-5% of policies, and average claim payments are $2,000-5,000.
The loss ratio—claims paid divided by premiums collected—for title insurance averages 3-7%. Compare this to auto insurance (60-70%), health insurance (80-85%), or homeowner’s insurance (50-60%). Title insurers collect premiums and pay out almost nothing in claims.
Where does the money go? AI analysis of title insurance company financials shows approximately 70-80% goes to commissions and kickbacks to real estate professionals, lenders, and settlement agents who refer business. Title insurance is essentially a legalized kickback system disguised as insurance.
Even more egregiously: AI has revealed that title searches—the actual work of verifying ownership—typically cost $200-400 to conduct. The title insurance premium of $2,000-3,000 isn’t paying for the search or for meaningful risk coverage. It’s paying commissions to everyone involved in the real estate transaction.
One comprehensive analysis estimated that if title insurance were priced based on actual risk and cost, premiums would be 60-80% lower, saving home buyers approximately $5-8 billion annually.
The Property Management Fee Inflation
Investment property owners often hire property management companies to handle tenants, maintenance, and operations. AI analysis reveals that property management fees have increased substantially while services have decreased, as technology automated much of what managers once did manually.
Here’s the pattern: Property management typically costs 8-12% of collected rents. AI analysis shows that this percentage has remained stable or increased even as technology automated rent collection, maintenance coordination, and tenant communication—tasks that once required significant labor.
Even more problematic: AI has revealed that property managers often profit from vendor relationships, receiving kickbacks from maintenance providers, insurance agents, and service companies. A plumber charges the property owner $500 for work that cost $300, with $200 going back to the property manager through referral fees or revenue sharing.
AI analysis of property management costs versus actual services provided suggests that legitimate management—tenant screening, rent collection, basic coordination—should cost 3-5% of rents with modern technology. The difference—3-7% of rents—represents extraction through opacity and captive relationships.
One estimate: property management fee inflation and undisclosed vendor kickbacks extract approximately $8-12 billion annually from investment property owners, who ultimately pass these costs to tenants through higher rents.
The Homeowner Association Expense Opacity
Homeowner associations (HOAs) are supposed to maintain common areas and protect property values. AI analysis reveals that many HOAs operate with minimal oversight, inflated expenses, and systematic favoritism toward management companies and service providers.
Here’s what AI discovered: HOA budgets often include management fees of 15-25% of total revenue—far higher than necessary for the services provided. AI analysis shows that professional HOA management consists primarily of bookkeeping, basic maintenance coordination, and meeting facilitation—services that should cost 8-12% of revenue with efficient operations.
Even worse: HOA management companies often have affiliated service providers—landscaping, pool maintenance, repairs—who charge above-market rates. Homeowners have no say in vendor selection and often don’t see comparative pricing. AI analysis suggests HOA-selected vendors charge 20-40% more than competitive market rates for identical services.
AI has also revealed systematic abuse in special assessments. HOAs impose one-time charges for major repairs or improvements, but the actual work often costs substantially less than assessed. The difference disappears into reserve funds controlled by management companies or gets used for projects that benefit management rather than homeowners.
One analysis estimated that HOA expense inflation and vendor markup schemes cost homeowners approximately $6-10 billion annually in unnecessary expenses—costs that many homeowners don’t scrutinize because the amounts are spread across monthly dues and occasional special assessments.
The Foreclosure and REO Property Information Gap
Foreclosed properties and bank-owned real estate (REO) are supposedly sold at market-clearing prices. AI analysis reveals systematic information concealment that allows insiders to purchase properties below market value while ordinary buyers remain unaware of opportunities.
Here’s the pattern: Banks acquire properties through foreclosure. These properties should be sold quickly to recover loan value. But AI analysis shows that REO properties often sit in “shadow inventory”—owned by banks but not listed for sale—for extended periods.
Why? Banks release REO properties slowly to avoid flooding markets and depressing prices on their other properties. AI analysis shows that banks with large REO portfolios in specific markets release properties 40-60% slower than banks with smaller portfolios, suggesting deliberate inventory management rather than efficient disposition.
Meanwhile, insiders with bank relationships learn about REO properties before public listing. AI has identified patterns where certain buyers purchase REO properties at 15-25% below eventual market value, flip them within months, and generate substantial profits. The information advantage—knowing what’s available before it’s publicly listed—creates systematic wealth transfer.
One estimate: shadow inventory practices and insider information advantages cost ordinary home buyers approximately $4-8 billion annually in lost opportunities and overpayment for properties that could have been acquired more cheaply if markets were truly transparent.
The Commercial Mortgage-Backed Securities Opacity
Commercial properties are often financed through commercial mortgage-backed securities (CMBS)—loans packaged and sold to investors. AI analysis reveals that CMBS markets operate with minimal transparency, allowing property owners and lenders to conceal problems while maintaining inflated valuations.
Here’s how it works: A commercial property owner gets a $50 million CMBS loan. The loan gets packaged with others and sold to investors. If the property later struggles—vacancy increases, income drops—the owner has incentive to conceal problems to avoid technical default.
AI analysis of CMBS performance versus underlying property performance shows systematic discrepancies. Properties report stable or improving performance to CMBS investors while actual occupancy and rent collection decline. The lag between reality and reported performance can extend 12-24 months.
Why does this happen? CMBS loans are serviced by special servicers who have limited incentive to discover problems early. Property owners provide financial statements that are often unaudited and unverified. By the time problems become undeniable, properties may be so impaired that recovery is impossible.
AI has also revealed that some property owners strategically default on CMBS loans—walking away from properties worth less than the debt—while maintaining paying loans on other properties. The CMBS investors bear the losses while the property owner’s overall portfolio remains profitable.
One analysis estimated that CMBS performance reporting lags and strategic defaults cost investors approximately $10-20 billion in losses that could have been mitigated with earlier intervention and greater transparency.
What Happens Next
Real estate has operated for generations on the principle that information asymmetry favors insiders—agents know more than buyers, landlords know more than tenants, commercial owners know more than investors. That asymmetry is eroding.
AI tools now exist that can analyze comparable sales more objectively than appraisers, identify inflated rents and unfair lease terms, calculate true costs versus pass-through charges, and reveal valuation fictions. The information advantages that real estate insiders relied on are disappearing.
The industry will resist. Real estate commissions are protected by professional associations. Title insurance is protected by state regulations. Property tax assessment inequities are protected by resource-limited assessors who can’t fight wealthy property owners. Commercial property opacity is protected by complexity that makes oversight impractical.
But the economic pressure is building. Buyers armed with AI analysis can see when they’re being overcharged. Tenants can identify unfair lease terms and inflated operating expenses. Investors can detect valuation fictions and avoid overpriced properties. Regulators can quantify assessment inequities and commission cartels.
Final Thoughts
The awakening in real estate and commercial property isn’t about whether real property has value—it obviously does. It’s about revealing that the systems for determining, reporting, and transacting on that value have evolved to maximize extraction by insiders while concealing risks and costs from outsiders.
AI is making visible what real estate professionals have always known but buyers, renters, and investors couldn’t easily verify: comparables are cherry-picked, valuations are inflated, commissions are excessive, expenses are marked up, occupancy is overstated, and information asymmetry is deliberately maintained.
We can do better. Transparent pricing, objective appraisals, competitive commissions, verified occupancy reporting, and fair assessment practices are all achievable. Other countries demonstrate that real estate markets can function with far lower transaction costs and greater transparency.
The age of real estate as an information asymmetry business is ending. What replaces it—whether reformed practices from existing players or disruption from technology-enabled alternatives—will depend on whether the industry embraces transparency or fights it until disruption forces change.
Either way, the valuation fictions are becoming visible. And once visible, they become indefensible.
In our next column: Media, Advertising, and Metrics—The Manufactured Reach.
Related Articles:
National Bureau of Economic Research – The Assessment Gap: Racial Inequalities in Property Taxation
The Wall Street Journal – Justice Department Investigating Real Estate Commissions
Urban Institute – How Airbnb Affects Housing Affordability and Availability
