The residential solar industry has been grappling with reputation problems for a while now. Predatory sales tactics, high-pressure door-knocking, inflated system sizes, and misleading savings projections have eroded homeowners’ trust over the past decade. When things go wrong, the industry’s default response is to blame the “solar bro”— the fast-talking rep who oversold a system and moved on to the next deal.
There’s a more uncomfortable conversation the industry keeps avoiding, and it starts with the role solar finance companies play in enabling the very problems they criticize. Third-party ownership (TPO) has become the dominant financing vehicle in residential solar. In the TPO model, production accuracy is the foundation of the entire financial relationship between the homeowner, the contractor, and the capital provider. When designs are inaccurate, everyone in that value chain pays a price. And the companies with the most influence over design standards are the ones writing the checks.
As CEO of Scanifly, a solar software company, I work directly with finance companies across the market. Many are building serious businesses with thoughtful, elevated standards. Some are genuinely trying to get it right. Others are either intentionally ignoring the long-term implications of their decisions in exchange for short-term growth, or they truly don’t understand how their practices affect the broader market.
There is clearly a spectrum of actors. But that spectrum creates systemic gaps in how companies evaluate project designs and photo packages and how they hold contractors accountable. Those gaps have real consequences for homeowners, for fund performance, and for the industry’s long-term viability.
Volume vs. Quality: An Honest Look at Incentives
Solar finance companies raise external capital, deploy those funds, and then seek new investments to repeat the process. Some keep those investments on their balance sheet, while others securitize them, which creates market liquidity but also encourages a shorter-term view of asset performance. Either way, the model runs on volume.
But it creates a tension the industry rarely discusses openly. The same companies that should be underwriting project quality also have incentives to move fast, onboard as many contractors as possible, and minimize friction in the submission process. Quality controls that add friction can start to feel like threats to growth. This is a difficult balance, especially when there is competitive pressure to lower prices in exchange for volume.
We’ve seen where this leads in other industries. The loosening of mortgage underwriting standards in the mid-2000s didn’t happen because anyone decided to be reckless. It happened incrementally, as each small compromise felt justified by competitive pressure. Solar isn’t there yet, but the structural conditions are similar: a rapidly growing market, limited regulatory oversight, and a small number of large capital allocators who set the standards everyone else follows.
Finance companies have the greatest influence on the quality of residential solar installations in the value chain. That influence carries a responsibility that growth targets alone should never override.
The Data Quality Problem Nobody Talks About
Most solar software platforms use LiDAR data to underpin their remote shading analysis tools. As a result, finance companies have come to depend on it.
The solar industry has built its sales and financing standards around remote data, and there are understandable reasons for this. LiDAR-based submissions enable sales reps to close deals quickly and easily due to the remote data structure, supporting higher project volume. Finance company review teams rely on LiDAR because it’s what they have access to. Many people at solar finance companies came from sales backgrounds or fast-growing contractors that relied on similar practices. It’s an echo chamber.
They fundamentally believe that LiDAR is the most accurate tool available. Many don’t know the history of solar shading technology, or the role that tools like the Solmetric SunEye and the Solar Pathfinder played in establishing objectively accurate on-site, real-time shade data.
So why is LiDAR problematic?
In many markets, publicly available LiDAR data is 5 to 10 years old. Neighborhoods change, trees grow. Old data produces inaccurate shade readings, which lead to inaccurate production estimates. At Scanifly, we’ve always published the date and point cloud density of the LiDAR data used in our analyses because transparency about data sources is basic due diligence. Most platforms don’t make this information easy to find, and most finance companies reviewing submissions don’t ask for it.
Before the One Big Beautiful Bill, and especially when interest rates were lower, shade data was less relevant because financiers were primarily underwriting homeowners’ creditworthiness. Now, shade data is paramount, since TPO funds are underwriting system performance directly.
Some regulators have long recognized this problem. Austin Energy does not accept remote-only analysis for certain applications. The Energy Trust of Oregon offers enhanced incentives for projects verified with drone or other high-accuracy site data. Finance companies have the same leverage to push for better. At a minimum, requiring disclosure of the data source and date would cost very little and could improve portfolio performance.
The Solar Shading Analysis Gap
Shading analysis is one of the most important inputs in a solar design. It directly determines production estimates, which determine the financial projections a contractor presents to homeowners, and ultimately, whether a homeowner’s actual savings match what the rep promised at the point of sale.
Every finance company maintains a list of software tools whose shading analysis and production forecasts they will accept alongside project submissions. Each finance company also has its own process for validating those tools. However, those validation processes vary widely in rigor, and the standards applied to incoming submissions are often less defined than they appear.
When Scanifly has participated in design validation exercises with finance companies, comparing our outputs with those of other platforms, we’ve encountered significant knowledge gaps among the reviewers conducting the analysis. The questions below are essential for making an apples-to-apples comparison between projects and ensuring that reviewers can accurately evaluate module placement, shading, and production. In practice, reviewers routinely leave them unanswered during the review process—sometimes intentionally, and sometimes out of ignorance.
| Question | Why It Matters |
| What module spacing and racking configuration should be assumed for a fair comparison? | Failure to specify this has direct implications for how many modules can physically fit on a given roof section, which affects total system size and production. |
| What fire setback requirements apply in this jurisdiction? | Setback rules vary by jurisdiction and directly impact the number of modules that can be placed, which in turn affects total production. |
| Which shade data layer should be used: LIDAR, Google 3D, or another source? | The choice of shade source has considerable implications for production output. Different sources vary significantly in age, point density, and accuracy. |
| What version of PVWatts, or which production simulator, is the reference standard? | Older simulator versions have been shown to overestimate production in some cases. Without a standardized reference, comparisons between tools are unreliable. |
Many finance companies built their underwriting processes around evaluating homeowner creditworthiness, which was the primary focus when loan products dominated the market.
In the era of TPO, the calculus is fundamentally different. Understanding a project design and its production implications requires different standards and different capabilities than underwriting a borrower’s credit score. That transition hasn’t happened consistently across the industry, and the gap shows up every time a finance company struggles to answer the questions in the table above.
Remote Designs & Shading Analysis Can Be Manipulated
LIDAR is already an imperfect data source for the reasons outlined above. However, the problem doesn’t stop there.
Sales reps and designers can adjust remote solar design environments in ways that reviewers cannot easily detect. And because finance companies have built their review processes around remote data—because it drives volume and it’s what they know—they don’t often catch it when someone manipulates that data.
The adjustments are straightforward. Designers can offset LIDAR height data along the Z-axis by 10 to 15 feet, meaningfully reducing the apparent shade impact of surrounding obstructions. Sales reps can shrink, reposition, or remove trees entirely. They can switch shade data layers to whichever source produces the most favorable production estimate, without any disclosure requirement. They can tighten module spacing and interpret fire setbacks more aggressively than local code allows, to fit more panels into a roof section.
Finance companies continue to rely on remote data because it enables volume. But they’re relying on a data source that they know, at some level, is adjustable and imperfect. The answer isn’t to eliminate remote analysis — it’s a practical tool, especially in early-stage design. The answer is to be honest about its limitations and to incentivize verified, on-site data that no one can manipulate after the fact. The reason most finance companies haven’t done this is straightforward: requiring or monetarily incentivizing on-site data slows down the submission process, and slower submissions mean lower volume.
The Monoculture Risk
To make matters worse, most finance company review teams use the same remote design tools that sales reps use to create submissions in the first place. When a contractor submits a Scanifly design or another on-site design with real-time, photorealistic data for review, the reviewer often evaluates it using an inferior dataset and a tool that wasn’t built for independent verification.
The result is that errors in shared platform methodology propagate everywhere simultaneously, with no independent reference point to catch them. Finance companies have the leverage to address this by accepting and prioritizing a broader range of design tools. Doing so reduces systemic risk and creates the competitive pressure that drives quality improvement.
The Problems with “Bankability”
In solar, bankability is the gold standard for validating software tools. If a third-party engineering firm like DNV or Black & Veatch has reviewed a software’s production modeling methodology and signed off on it, the industry considers that software “bankable.” Finance companies use this as a primary filter for which design tools they’ll accept.
On the surface, this sounds reasonable. In practice, it has some significant flaws.
The conflict of interest problem.
Companies seeking a “bankability” certification hire and pay third-party engineering firms that review them. This structure mirrors how credit rating agencies like Moody’s or S&P operate in traditional capital markets. The engineering firms conducting these reviews aim to be rigorous, but the incentive structure is worth acknowledging honestly.
The expiration problem.
Some software platforms received bankability certifications five or more years ago. Software changes, updating the production simulations, weather datasets, and LIDAR data. When a finance company requires bankability as a condition of partnership, are they verifying that the original certification still reflects the software’s current methodology? In most cases, it appears they’re not. If bankability really matters, companies should revalidate it regularly rather than treat it as a permanent badge.
The barrier-to-entry problem.
Obtaining a bankability certification is expensive, often a high six-figure cost. This puts some innovative, well-engineered software companies at a structural disadvantage. The result is a market where bankability functions less as a quality signal and more as a barrier that protects incumbents. If finance companies want accurate, well-designed systems in their portfolios, reducing this barrier or investing in their own internal validation capabilities would serve everyone better.
What Better Solar Financing Standards Look Like
None of these issues requires regulatory intervention to fix. In fact, we hope the industry addresses these issues before the government gets involved. In some states, they already are.
Rather, finance companies have the tools, the data, and the leverage to solve these issues. So here are a few practical starting points.
Standardize design inputs for project review.
Require clear disclosure of data sources, production simulator version, module spacing assumptions, and fire setback methodology. Without standardized inputs, comparing tools is meaningless.
Incentivize more accurate site data.
Offer better terms or streamlined approval for projects that include drone-verified designs or other on-site, real-time data sources. This already works in regulated utility programs.
Build internal expertise.
The most effective long-term solution is deeper in-house knowledge of solar system design and production modeling. Everything else works better with it.
Diversify the tools in your ecosystem.
Accepting a broader range of design platforms adds some operational complexity but reduces systemic risk and drives quality improvement.
Re-evaluate bankability on a regular cadence.
If a platform received certification five years ago, require updated validation. A certification for a previous version of a product isn’t fulfilling its purpose.
The Bigger Picture for Solar Finance Companies
Only a few finance companies control the majority of TPO solar financing in the U.S. residential market today. That concentration of market power comes with a concentration of responsibility. These companies set the standards that contractors, EPCs, and software providers align to. They define, in practice, what quality means in this industry.
Every system that underperforms its projections and every homeowner who feels misled about their savings, makes it harder for solar to earn the long-term trust of American consumers. The sales rep who closed the deal gets most of the blame when things go wrong. But the finance company that funded the project without rigorous design review shares the accountability.
The path forward isn’t complicated. The standards exist, the tools exist, and the data is available. The industry needs to hold the process to a higher bar — not because regulators require it, but because its long-term health depends on it.
Frequently Asked Questions
What is bankability in solar?
Bankability is a validation exercise in which a third-party engineering firm has reviewed a solar software’s production modeling methodology and deemed it reliable enough for financial institutions to accept. In practice, these evaluations are expensive to obtain, rarely revalidated after the initial review, and issued by firms hired and paid by the companies seeking certification. It’s a useful concept in theory, but the current implementation has real limitations.
How do solar finance companies review project designs?
Most finance companies review project submissions by checking designs against a preferred software platform, typically the same one the contractor used to create the design. Reviewers may verify system size, shading analysis outputs, and production estimates, but rarely require standardized inputs like data source, simulator version, and fire setback assumptions. This makes it difficult to catch errors or compare tools on a fair, apples-to-apples basis.
Why do some solar systems underperform their projected production?
Underperformance usually stems from inaccurate shading analysis or production modeling during the design stage. Common causes include outdated LIDAR data, inconsistent production simulator versions, and remote design environments that sales reps and designers can adjust in ways reviewers may not always catch. When finance companies don’t require disclosure of these inputs, inaccurate designs can clear approval without meaningful scrutiny.
What is the difference between LIDAR and drone data in solar design?
Most solar software pulls LIDAR data from government or municipal aerial surveys that are often 5-10 years old. Some updated sources fill in gaps, but coverage remains limited. Drone-captured data, by contrast, comes from the site itself, reflects current conditions, and produces significantly more accurate 3D models of the roof and surrounding obstructions.The difference matters most in shading analysis, where tree height, roof geometry, and nearby structures directly impact production estimates.
What is PVWatts, and why does the version matter for solar system production?
PVWatts is a production modeling tool developed by the National Laboratory of the Rockies that most solar software platforms use to estimate system output. NLR updates the tool periodically, and newer versions incorporate improved weather data and modeling methodology. Older versions have been shown to overestimate production in some cases. When finance companies don’t specify or verify which version of PVWatts a platform is running, they may be approving projects based on outdated calculations.
