The brokered patent market in 2019

Since average price per patent was the “worst thing that has ever happened to patents”, we are pleased to introduce the new worst thing ever

As ever, there is good news and bad news in our report. Overall, asking prices are up from last year, exceeding even the 2017 numbers. However, the size of the market is down and for those on the inside of corporations, it looks as if assertion activity from NPEs may be on the rise.

As the market matures, things continue to change. The way that buyers and sellers transact is shifting to more private deals. To adjust to this, it is now more crucial than ever that buyers have a system in place to manage the pipeline of deals for sale. Due in part to this shift towards private sales, the brokered market has shrunk by nearly every metric: the number of deals listed, assets listed and deals sold, and even our estimate of the number of full-time brokers. When it comes to specific litigation needs, buyers are focusing on direct, private deals, while for risk removal, they appear to be looking at lower-cost direct auctions.

This article covers broader market factors, including the following:

  • Sales decreased to $300 million, down from $353 million last year, and about the same as two years ago – although private patent sales are up.
  • Asking prices rebounded 56% from last year’s staggering drop of 30%, although much of this swing can be attributed to price fluctuations in single asset deals.
  • Software sales continue to dominate, accounting for 52% of sales.
  • Old deals still sell – multiple packages sold even though they had been on the market for three plus years.
  • NPEs are buying a larger share of the sold brokered deals – although NPE litigation is down overall, litigation threats from sold packages continue to rise.

In the past, we have put our analysis in perspective by looking at the numbers from the viewpoint of a particular participant in the brokered patent market. While continuing to do this, we have introduced an additional focus on how a buyer or seller might better understand and use the data. Each metric measured has a distribution curve, a variance and other quirks that differ from a bell curve. (For those that have been following our reports, you will likely be unsurprised that the brokered market does not fit a bell curve.) When working with a normal distribution, the median, average and standard deviation provide significant insights. In contrast, many of the distributions here need examination with additional descriptive statistics in order for meaningful conclusions to be made. While we do not have enough space to go into this level of detail for every statistic in the market summary, we encourage you to consider additional questions about the data and the ways in which asking these can increase your perception. In addition, as an adjunct to this year’s report, we have expanded our explanations of how a buyer or seller might use this data to better price their deals.

After eight years of tracking the patent market we now follow more than 195,000 assets across more than 8,100 deals. When analysing a deal for a potential purchase, it is possible to normalise deals across specific variables, create comparable groupings and enter negotiations armed with more accurate information. This is why we started tracking the patent market.

Market size

If you add up the asking prices of all the assets tracked in our database, you get about $25 billion of patent assets for sale. We use programs to parse the assignment records for these assets and have identified sales of $6.2 billion – almost $1 billion more than our tracked sales at this time last year. The brokered market may have shrunk, but the secondary patent market is still big business.

Figure 1 shows the market that we have tracked for the past eight years. We include both private and public packages and try to determine an overall total dollar value for the patent market. Visibility into private packages continues to expand quickly (you can see the jumps in 2018 and 2019 data) but remains limited to transactions where we have clients or market report participants. Although this visibility changes each year depending on our client mix, we can confidently say that we have seen an expansion in the private market. That said, the dollar value of the secondary market overall is surprisingly large and active.

Figure 1 also shows an extrapolation of the market through the third quarter of 2020. Historically, we have seen between about $1.5 billion and $3.75 billion in new potential packages enter the market every year. However, in the past two years we have seen approximately $5.3 billion and $6.7 billion, respectively. Numerous assets are on offer and there are more ways to buy beyond the brokered market than ever before; IP3 through AST, the IAM Market and OceanTomo’s BidAsk programme are all ways to source deals.

The sales data as of the third quarter of 2019 includes only sales for which we have identified an assignment document. Projecting through 2020, we expect cumulative total sales to reach just under $9 billion.

The remainder of this article follows the flow of a typical purchase process, covering sourcing, asking prices, diligence steps, purchase closing and litigation. It then concludes with our estimate of the market size.

Patent brokers

In the past, we have used analogies to describe why using a broker or adviser matters if you have never participated in the patent market. This year, we skip the analogy. Simply, if you have never participated in the patent market, use a broker or other experienced adviser. As one of our law professors once said: “Yes, sure. You might be smart enough to set your own leg. Why would you want to? Hire a professional.”

Brokers have networks of connections enabling them to find the most diverse sets of patents for sale from a variety of sellers and determine who is buying. More importantly, they often know who can get a deal done within different companies. On a cautionary note, like the real estate market before the advent of tools such as the multiple listing service, Zillow and Redfin, a broker may have visibility into its own listing portfolio and network but not much beyond that. Only those who have non-disclosure agreements in place with multiple brokers and the money to buy patents can access the full market information, including thousands of patents for sale; otherwise, you can only see what your broker does.

Again, as in real the real estate market, some brokers are more successful than others. A great broker not only has a passion for buying and selling patents, but can offer a skillset that most market participants simply do not have. This includes the ability to:

  • filter patent assets to identify which ones to sell;
  • select viable sellers and buyers;
  • screen patents and identify those that are important, as well as their claims (this can be crucial for cutting diligence costs by allowing buyers to focus on the most important parts of a package first);
  • provide pricing guidance;
  • provide guidance for sellers with regard to sales terms and timelines;
  • define the process for diligence, bidding and sales;
  • develop evidence of use (EOU) materials; and
  • negotiate pricing.

What is more, brokers have the experience to get deals done. Many maintain a network of hundreds of potential buyers and make it their business to know who is buying or selling certain technologies and on whose desk a deal needs to land. For buyers, brokers can seek out assets for your specific buying needs; while for sellers, good brokers can manage the sales process when only a small portion of potential buyers have any interest. Clients frequently ask us if they should work with a broker or sell directly. We evaluate that client’s skillset as if they were a broker to determine whether they have the means to get a deal done. Often, they do not, but when they do, the skills are usually found in a company’s corporate development department.

Figure 1. Cumulative sum of asking prices in the brokered and tracked private market

Figure 1. Cumulative sum of asking prices in the brokered and tracked private market

Table 1. Brokers listing five or more packages in the 2019 market year

Adapt IP Ventures

AQUA Licensing, LLC

Blackhawk Technologies, LLC

Dynamic IP Deals LLC



IP Offerings

IP Trader

IPInvestments Group

Ocean Tomo, LLC

Quinn Pacific

Red Chalk Group

Reliance Capital

Rui Zhi Ventures Limited

Tangible IP

TransactionsIP LLC

Brokers with five or more packages

The total number of brokers has dropped to 47 this year (from 56 last year) – and continues to fall. Three years ago, there were 74 brokers listing a package. The recent decline is likely driven more by the lower volume of deals on the brokered market than further concentration. In addition, “for sale by owner” listings have dropped at a greater rate than the overall market has shrunk – now accounting for 4.5% of the packages on the brokered market (down from 5.9%). We believe that fixed-fee auctions such as Allied Security Trust (AST)’s IP3 programme continue to gain popularity with individual inventors who choose not to use a broker.

While the total number of brokers has decreased, the concentration of market share remains the same. A small group of brokers continues to bring the majority of packages to market; 23% of brokers (11 of 47) brought 10 or more packages to market this year. This is virtually unchanged from the 25% (14 of 56) that did so last year. Of packages listed through brokers, 88% were listed through the 16 brokers in Table 1 that brought five or more packages to market. Brokers listing five or more packages accounted for 83% of brokered packages last year. Likewise, the top four brokers listed 56% of all listed packages – up from 50% last year. Overall, 14 of the 16 brokers in Table 1 were also in last year’s list. Thus, the top brokers seem to have locked down their operations and their market share. However, while further concentration of deals with top brokers seems inevitable, we have not seen this yet.

With the exception of brokers affiliated with semiconductor reverse-engineering houses or other hardware focus, we see little technological specialisation among brokers. Instead, they have operationalised how they market their listings in a slightly different way. Some send out newsletters with summaries of new listings hoping to draw buyer interest, while others send out full marketing decks for every package. Just like the methods, the relative success of brokers varies greatly.

Figure 2 shows the success of individual brokers (each broker being a dot). The x-axis represents the number of packages brought to market during the measurement period. We used the 2018 calendar year for this analysis to allow sufficient time for sales to close and be recorded. The y-axis represents the sales rate or closing rate for that broker (the number of packages sold) – the higher to the right, the better. As shown in Figure 2, a few brokers were particularly successful (green circles) or unsuccessful (red circles). Importantly, almost all the dots move up over time, meaning that all the brokers are experiencing higher closing rates when packages have more time to sell.

Selling patents is not easy, but some brokers have consistently enjoyed more success than others. Of the packages listed in the 2018 calendar year, 10% have sold. Compared to previous years, this is a drop from the 2017 listings’ broker sales rate of 13.5% but consistent with the 2016 listings’ 10.9%.

From a wider perspective, the total number of brokered packages sold in a given market year has been generally climbing (see Figure 3), but at first glance appears to drop off in 2019. This was initially concerning, especially considering the high number of packages listed last year, which we expected to result in higher sales the following year. However, on further analysis, we believe that this can be explained by two factors. Most significantly, we removed the more than 2,300 single-family packages listed by Provenance IP Group in the 2018 market from our analysis this year and last year, because this listing method is an atypical event. Provenance has subsequently closed two large transactions that include 136 of these single-family packages. We believe that the drop in package sales is explained by considering these large transactions, along with an increased shift towards private transactions and fixed-fee auctions. We will discuss both Provenance IP Group and sales rates shortly.

Figure 2. 2018 broker sales rates by number of listed packages

Figure 2. 2018 broker sales rates by number of listed packages

Figure 3. Actual sales by market year of sale

Figure 3. Actual sales by market year of sale

Figure 4. Package distribution by technology group

Figure 4. Package distribution by technology group

Table 2. Brokered patent market contents




% change





US-issued assets




Total assets




Other market opportunities

There are always new ways in which to buy or sell patents. Some combine the existing skills of brokers with new platforms, while others are completely new models:

  • IAM Market is a market provided by IAM where sellers can list their patents and anyone can browse the packages, contact the sellers and close patent purchases.
  • IP3 is a fast-close patent buying programme run by AST. Sellers list their assets for a set price and AST member companies decide whether to purchase – all on an accelerated schedule.
  • Provenance Asset Group and Intellectual Ventures continue to divest their portfolios.

None of the packages listed through these programmes were included in our overall dataset. IP3 is a private buying programme, while Provenance’s assets, as with all packages (200 or more assets), were excluded.


Between 2017 and 2018 we saw a significant increase in the number of listings and the total contents of the brokered market. However, this year saw the reversal of that trend. With 448 packages, the 2019 market listings have fallen by 24% (see Table 2) back to the 2017 numbers. The drop in packages, US-issued assets and total assets of 24%, 42% and 36%, respectively, simply undoes the increases of 18%, 34% and 43% from 2017 to 2018.

We have benchmarked our deal flow with that of other large corporations and defensive aggregators, and have found that the number of brokered packages that we received is generally similar. Therefore, we are confident that our numbers reflect the market. Further, while we limit the types of package included in this dataset to the more common types (eg, quasi-public/brokered packages containing 200 or fewer assets), we continue to track larger bulk deals and private deals.

The market provides buying opportunities in a diverse range of technologies, affecting various products and focus companies. There are assets available to fill business needs in almost any high-tech category. When we receive a package, we use the package materials and any assets highlighted by the seller to categorise it according to our taxonomy of technical areas. We have developed a two-tier classification taxonomy with 17 general technical categories and 108 sub-categories. We regularly modify this as new technologies come onto the brokered market.

As illustrated in Figure 4, software continues to be the largest technology grouping. There has also been significant growth in certain software technologies, including increased listings relating to the Internet of Things, internet-scale data management, AI, machine learning and content and consumer software such as video and image processing. In addition, the “other” category has held its position over hardware as the second-most common category. This is due to the continued popularity of listings relating to automotive and energy sectors, as well as the steady rise in medical device listings.

Figure 5. Word cloud of hot companies, technologies and products

Figure 5. Word cloud of hot companies, technologies and products

The word cloud in Figure 5 provides another way to visualise the focus of the brokered patent market and gives a sense of how most packages were marketed in the 2019 market year. The relative sizing of the words highlights the hot companies, technologies and products identified in the summaries of EOUs provided as marketing material from the broker or seller of the packages. It should come as no surprise that the biggest technology companies (eg, Microsoft, Apple and Google) remain the favourite targets of patent sellers’ EOUs. For the third year in a row we have seen an increase in references to Facebook, which is now as prevalent in EOUs as the others. Cisco also rose in the ranking.

Package sizes

The distribution of package sizes (see Figure 6) continues to be one of the most consistent metrics describing the brokered market. There are fewer very large packages and most deals fall into the single asset or <=5 asset buckets. As there is little change to discuss, the consistency provides an opportunity to explore the ways in which deeper analysis of a distribution can provide a more actionable story. Figure 6 is a right-skewed distribution; most of the data is to the right-hand side of the peak. In fact, it is even more right-skewed than it appears because the size of the x-axis increases as we move to the right. The impact of this is similar to – although not as drastic as – a logarithmic scale. This type of distribution is typical of the brokered market because packages cannot go below $0. By definition, every package must have at least one asset which could be listed quite cheaply.

In contrast, there is no high-side limit on how much you can spend on a patent or how large a deal could be. Because this is not the normal (bell curve) distribution that we are used to seeing, our typical intuitions about statistics may give the wrong impression about parts of the brokered market.

In a normal distribution, the average (ie, the mean) and the median are the same value. They are not interchangeable, but we can think of either of them as a typical outcome. In a right-skewed distribution, the median is always less than the average. In 2019, the median number of assets in a package was four; 50% of packages had four or fewer assets, while 50% had four or more. However, the average was 14.2 assets. The average deal size was actually the 77th percentile and therefore not typical of a deal in the dataset; the vast majority of deals had fewer assets than the average. Nevertheless, the average is important; in this case, it highlights the expected values of a package size. If you closed 100 deals, you would likely acquire around 1,420 assets because you would have lots of small packages and a few big ones. The median, on the other hand, pinpoints what a typical deal size would be if you were doing one deal. Therefore, it is important to decide which descriptive statistics to use for a given model.

When making a one-off decision with a right-skewed distribution, we usually create models using the median value. What is more, we use percentiles to determine the probability of getting a value within a given range. For the 2019 market, there is:

  • a 25% chance of having a single asset deal;
  • a 50% chance of having a deal containing two to 12 assets; and
  • only a 7% chance of having a deal with more than 50 assets.

We could calculate the typical price per asset in one deal in the same manner. But when modelling for multiple occurrences (eg, when estimating the total dollars spent on purchased deals without pricing guidance), we use the average. Using the median would drastically underestimate the total. We will go into this in further detail using package pricing as an example.

Table 3. Asking prices in the 2019 market


Per asset

Per US-issued patent













Standard deviation



Numerical data




Figure 6. Distribution of package sizes (total assets)

Figure 6. Distribution of package sizes (total assets)

Figure 7. Average price per asset by package size

Figure 7. Average price per asset by package size


It is impossible to get a deal done if the parties cannot agree on the price. As a buyer, you will not be taken seriously if you significantly underbid. However, buyers want to get a fair price. Conversely, if you are a seller who will not negotiate near the market price, it will be almost impossible to close a deal. Patents are by definition unique and have varying relative strengths and market applicability, while the demand for them varies by technology area and many other factors. These conditions help to guide pricing models. In the past, we recommended using average or median pricing as a starting point for discussions and adjusting for specific factors related to the deal. By doing so, buyers’ bids will be seen as sane and no reasonable seller will walk away without a real negotiation. In many cases, starting with the average price and moving up or down from there is an effective way to set an initial price for buyers and sellers.

We believe that the availability of pricing data creates liquidity in the market; the more visibility there is into pricing, the easier it is for new and established participants. However, it is important to remember that the average asking price is a guide, rather than an absolute rule. We have helped clients to buy patents priced at more than $1 million per asset, which is well above the average market price, and have negotiated deals for a small fraction of that price. In both of these cases, we think that the prices were justified and, importantly, the deviation from the average was supported by deal-specific factors.

In 2019 the average asking price per asset rebounded significantly, rising by 56% from $124,000 per asset to $194,000. The asking price per US-issued patent also increased sharply, rising by 58% from $177,000 to $280,000. The latest asking prices are closer to those in the 2015 and 2016 market years. As a seller, you may be ready to pop the champagne – and frankly, go for it. This market can be unexpected. As for prices, the 2018 price drop appears to have been an anomaly with 2019 prices rising across the board.

Pricing method: normalised and adjusted for comparables

In this example, we want to purchase a business process package with 33 assets. We are going to walk through how to create a starting point for this negotiation by calculating an estimated sale price for the package. In this case, we are normalising for package size and market year.

In 2019 there were 26 business process packages with non-outlier pricing data. Unfortunately, there were only three in the 26-50 asset range. Three deals are not enough for a valid comparable analysis, so we would like to expand the comparable dataset to the full 26.

First, calculate the asking price per asset for each of the 26 business process packages.

Second, determine the average price per asset for each package size tranche (see the 2019 data in Figure 7).

Third, for each of the 26 packages, divide the asking price per asset calculated in step one by the appropriate average asking price per asset calculated in step two (eg, if the business process package had three assets, divide the calculated asking price per asset by $198,000 (see Figure 7, 2019, <=5)). The result of this step will be a percentage of the average for the package size tranche.

Fourth, average the 26 values (percentages) calculated in step three to find the average premium or discount for 2019 business process packages. In this example, we get 84%, which is a 16% discount off the average.

Fifth, the package that we are buying has 33 assets and the average asking price in Figure 7 for that package size is $95,000. Therefore, the average asking price is $95,000 per asset times 33 assets, which equals $3.135 million. Multiply $3.135 million by 84% and then apply our standard 35% discount from asking price to sale price. Expected sale price for this package is thus $1.7 million.

Looking back at Figure 7, if you are a seller, in most cases it makes sense to take the time to find and highlight key patents and then group these into smaller packages in order to increase the overall price per asset. However, breaking apart a portfolio is not always the best method; you may not have enough high-value assets to act as value drivers in each of the smaller portfolios. In this case, you would be left with undifferentiated unsellable assets that, in a larger package, may still drive some value for a buyer. Under specific conditions, bigger packages may provide a better selling opportunity. Making this decision will be difficult and can benefit from up-to-date knowledge of the market as well as in-depth knowledge of the specific portfolio. If you are a buyer presented with a large package, do not assume that you must accept it as it is. The seller will likely allow you to cherry pick assets out of the bulk lot. But in that case, the price for these differentiated assets may increase. When approached by a buyer looking to purchase one family from a large lot, some sellers may have a minimum package price to make a deal worthwhile. In these cases, the parties often add assets to the deal in order to make the price per asset palatable to both sides.

Per-asset pricing by package size

Asking prices rose across most package sizes (see Figure 7). The largest increase came from single-asset packages nearly tripling by 197%. Last year the asking prices for single-asset packages fell by more than half (56%), which resulted in 2018 being the only year in which we have not observed an asking price premium for single assets. These packages have rebounded and then some. The shift can also be seen in Figure 8, where there are far fewer packages with asking prices of less than $250,000. However, this is where problems emerge with average pricing. If you sold a single asset, you got a lot more than a 56% increase, but as discussed previously, single-asset packages make up only 25% of the 2019 brokered market. If you sold a package with more than one asset, three-quarters of the time, you got a 34% increase in price, rather than 56%. Average pricing is only a starting point.

Here, we want to address a conference attendee who once told us: “Average price per patent is the single worst thing that has ever happened to patents.” Well, if you hated that, you may now have something even worse – namely, median/average pricing that has been normalised and adjusted for comparables. Our complaints box is open.

The gold standard for pricing information has always been comparables. The big accounting firms and investment banks live and breathe this methodology, and they derive a fair market price. Where there are a small number of comparables, the goal is to determine market price (as the market is large enough to determine what fair is). The brokered market is still opaque and sample sizes can be small. Prices are affected significantly by package size, EOUs, the year in which a package was listed and technology area. What is more, these variables are not independent. Typically, you would solve this issue by sampling the data, but how many “business process” packages with 26 to 50 assets and an EOU were listed in 2019? Not enough for a statistically significant sample. Because we have tracked the market and now have a robust dataset, we can isolate the effects of a variable by normalising out the impact of other variables. This allows us to increase our comparable sample size and improve on the typical average pricing model (see “Pricing method: normalised and adjusted for comparables”).

It is possible to normalise for many variables at once, provided that each data tranche is large enough to create a valid dataset. As our database grows, so does our ability to ask and answer more specific questions. Throughout the remainder of our discussion on pricing, bear in mind the possible uses of these techniques.

Packages with pricing guidance

This year only 50% of packages came with specific pricing guidance, down from 76% last year. One significant reason for this is that more and more packages come in with the commentary that the seller is expecting “market price”. This may be a sign that in a more mature market all parties are typically in agreement on pricing, but we believe that specific pricing guidance clarifies expectations for both buyers and seller and improves transparency in the deal process. In addition, only 33% of packages with pricing guidance had exact asking prices rather than ranges (eg, “mid-six figures”). Clear communication on all deal terms helps buyers to make decisions; without guidance, sellers increase the chance of a misunderstanding that may result in a lost sale.

Figure 8. Distribution of package asking prices (top and bottom 5% removed)

Figure 8. Distribution of package asking prices (top and bottom 5% removed)

Figure 9. Average asking price per asset by technology group

Figure 9. Average asking price per asset by technology group

Figure 10. Application software price per asset distribution – 2018-2019

Figure 10. Application software price per asset distribution – 2018-2019

Asking price by tech category

Technology categories continue to drive asking price variations but the signal for specific tech areas is often lost in the noise of other factors. For specific technology areas, we have updated our process to use the normalisation procedure described in “Pricing method: normalised and adjusted for comparables”. More generally, the major tech groupings have moved closer together in pricing. Rather than adjusting the price, we are starting to see sellers limit listings in some technology. When it comes to top asking prices, the “other” category has taken over as the highest priced category. This is due to the strength of automotive, energy and imaging prices in that category. Notably, communication packages have started to catch up with the rest of the pack.

Analysing the distribution of prices for specific technology areas can be helpful. Due to the nature of the data, the distribution of asking prices in a specific technology area is almost always right-skewed. Figure 10 shows the distribution of the asking price per asset for “application software” packages in the 2018 and 2019 market years. As a buyer exploring a package, you may want to ask yourself: “How valuable are these assets compared to others in the technology area?” Top assets demand a significant premium; 5% of packages had a per-asset asking price greater than $425,000 ($280,000 above average) but 50% packages were listed between $50,000 and $173,000 per asset. Thus, if a seller is asking for more than $173,000 per asset, it is important for them to explain why. Similarly, if a buyer is only offering $50,000 per asset, there should be a conversation about the reasoning behind this.

Often when price expectations are misaligned, it is because one party is confusing value to them and market price. This leads to frustrating negotiations and missed opportunities to optimise deals. What happens when someone substitutes the value for the market price? People set unreasonable price expectations, miscommunicate why assets might be valuable in a particular situation and have difficulty benchmarking their results. Let us start with the seller. If you find yourself thinking “these assets are really useful to me against XYZ company, so they must be valuable assets to others”, it is crucial to remember that this measure of value is specific to you and independent of the market price. Unfortunately, in this example, the technology area is particularly low priced. As such, the value to the seller is high but the market price is low. If the seller confuses the value to themselves and the market price, they risk getting no deal at all because their price expectations are misconstrued.

Value should always be evaluated in context (eg, “As the seller, value is determined from how I would use this asset in the future – what is that worth to me today?”). The market price is simply the price that the market will bear. To sell assets at a good deal, you must:

  • define the current value of those assets in the context of your specific situation; and
  • understand what the market price is at the time of a transaction.

Getting an optimal deal requires thinking about the value of the assets to potential buyers. Switching to the buyer’s perspective, if you think “we got a good deal because we got a 50% discount off the asking price”, you may be substituting the market price for what the value is to you (how do you know that you got a good deal if you did not determine what the value is to you?). Our data helps to identify what the market price is, but only a deeper strategic analysis will determine the value to you. (For an example of this, see “Strategic Counter-Assertion Model”, Richardson Oliver Insights, IAM 72.)

When looking at right-skewed graphs like Figure 10, it is crucial to understand the severity of the skew. In some cases, an extremely long right tail can pull the average all the way up to the 80th percentile. If this is the case, the price in this technology is highly sensitive to factors other than technology area (on the high end). On the other hand, if the data is tighter and the average and median are fairly close together, then price expectations are more consistent and negotiations with a reasonable seller should be more straightforward.

It is possible to get even more specific by first using the normalised and adjusted for comparables pricing approach, and then analysing the resulting distribution in a technology area. This in-depth analysis allows you to ask specific questions that are unanswerable with averages and medians alone. Are there specific traits that are common to “application software” packages priced in the top 25%? Now you can find that out.

Asking price and impact of EOU

EOUs are a great way for a seller to show that their package should fetch a higher price than average. Overall, the percentage of deals with EOU is down slightly, at 36% in 2019 versus 42% last year. The price premium for EOUs varies greatly by package size and is another opportunity to dig into normalised data. Over the full dataset, we observed a 27% price premium for packages with a seller-supplied EOU over those with no EOU provided. We also know that sales rates are higher for deals with EOUs (see below) so the value of writing an EOU is compounded.

Table 4. Sales rate by package size in 2018 listing

Package size

Sales rate
















Table 5. Repeat sellers (sold in 2018 or 2019)

2s Ventures

Allied Security Trust (AST)


Concert Technology

Delphi Corporation

Foxsemicon Integrated Technology, Inc

Harris Corporation

Hewlett Packard Enterprise (HPE)

Hewlett Packard Inc (HP Inc)

Hon Hai Precision Industry Co, Ltd

Huawei Technologies Co Ltd

Intel Corporation


Mirai Ventures LLC

Panasonic Corporation

Pendrell Technologies

Seiko Epson Corporation


Sisvel International SA

Sony Corporation

Technicolor, Inc

Videa, LLC

Figure 11. Cumulative percentage of sales by months from receipt date (2018 listings)

Figure 11. Cumulative percentage of sales by months from receipt date (2018 listings)

Figure 12. Cumulative percentage of sales by months from receipt date (2018 listings)

Figure 12. Cumulative percentage of sales by months from receipt date (2018 listings)

Figure 13. Percentage difference between Alice-affected sales rate and total market sales rate

Figure 13. Percentage difference between Alice-affected sales rate and total market sales rate


For the first time since 2016 we have seen a drop in the total transaction volume on the brokered market (see Figure 3), but this is not because transactions are not occurring. The 146 transactions still represent more deal flow than in 2014 or 2016. Moreover, purchases through other buying pipelines are increasing.

Older deals are also continuing to sell – including a deal listed in 2015. Indeed, sales from 2016 listings have outstripped our initial projections. Generally, highly sought-after packages move fast, followed by a long tail of additional sales. As a seller, patience can pay off.

We began tracking sales in order to avoid presenting sold deals to our clients. We wrote code to parse the USPTO assignment data and identify deals that were no longer on the market. This has enabled us to analyse what was selling and who bought it. Our methodology considers a package to have sold if at least one patent in that package is found to have an assignment corresponding to a sale. We use the execution date of assignment for the earliest transacted patent in the package as the date of the sale (data is limited to packages received before 31 May 2019 and sales recorded with the USPTO before 5 August 2019). When discussing sales, we switch to a different dataset, which includes 3,699 packages with 959 identified sales and is measured on a calendar year basis. This sample includes packages that were analysed in our previous papers and refers to packages listed as early as 2011.

Our sales rate for 2018 listings within a year of listing currently stands at 13.1%, which is slightly lower than last year’s rate of 13.9% for the same period.

The sales from 2016 are outpacing our predictions, while the 2017 disposals are tracking fairly closely. Taking this into account, we predict the sales rates of 2018 listings to be in line with the rates observed for 2016 (see Figure 11). We estimate that for 2018 listings the sales rate for packages on the market for one year will increase much above its current level of 13%. We also created a projection of the future sales for 2018 listings for an additional two years.

Please note that due to the time to sale, all sales data lags behind the listings market by 18 months (and potentially longer).

Sales by package size

We analysed the sales rate based on the size of the package listed and found – to our surprise – that package size had only a limited impact on sales rates (see Table 4). The sales identification methodology skews towards identifying sales of larger packages because if any asset in the package sells, the package is considered sold. Therefore, we can conclude that smaller packages outperform their larger counterparts. Buyers are still generally focusing on assets in smaller packages. Single-asset packages and packages in the two to five asset range continue to sell better at a rate of 12%. Last year we reported that the 2017 listings, at a similar timeframe, sold at a rate of 6% for single assets and 11% for the two to five range. These numbers are sales rates rather than numbers of sales, so the lower listing rate in 2018 may be helping its sales rate.

Sales by receipt date

When buying a package, moving quickly can be advantagous. This enables you to review all packages before the sell and ensures that you do not miss an opportunity. However, we know that corporate decisions include a lot of sign-offs, which take time, so how fast is fast enough? We analysed how quickly the sold packages listed in the 2018 calendar year transacted in order to estimate how much time buyers had to bid. It seems that buyers have lost some of the urgency that we saw last year. Figure 12 shows that 80% of the sales from 2018 listings occurred within 10 months from the receipt date of the package (up from between six and seven months for the 2017 listings). Even the fastest movers slowed down, with around one-third of the packages sold in the first six months; last year, one-third of packages sold in only three months. Despite this, accelerated decision making is still beneficial. If possible, getting a budget for patent purchase pre-approved by the board can help you to buy quickly and gain access to the widest variety of packages. It also facilitates an easier deal close.

Figure 12 considers the cumulative percentage of sales by months from the receipt date in regard to the 2018 calendar year listings, which have had at most 18 months on the market. As time moves on and additional packages sell, the earliest sales will make up a lower percentage of the total sales. When we look at an older dataset (ie, the 2015 listings), we see that 55% of the sales occur in the first year. While moving fast is still important, there is also a long tail of later sales, which shows that a slow trickle of sales exists for years after packages are listed. Overall, these timeframes act as a guideline, but there is no substitute for an efficient deal pipeline. If you are trying to limit NPE risk and they buy a package before you review it, it is simply gone.

Sales by provided EOU

This year, we have continued to see an increased sales rate for packages with a seller-provided EOU. In fact, packages listed in the 2018 calendar year with EOUs were 29% more likely to sell than packages without. In addition, buyers seem to be less resistant to acknowledging the value of a broker-provided EOU. EOUs confirm that the technology is adopted, act as a guide for developing your own EOU on a different product and quickly direct potential buyers to the value drivers in a deal. By combining the increased likelihood of a sale and the 27% sales price premium associated with EOUs (discussed above), the expected value of a package with an EOU is 64% greater. This is a smaller impact than we saw last year, but as more packages sell, the effect is likely to become more pronounced.

Life after Alice

Alice-affected software and financial packages continue to thrive, with sales rates staying well above the market average. Figure 13 reveals that packages from technology categories affected by the Supreme Court decision and listed in 2016 are 53% more likely to sell than packages in the overall market. The trend has continued to a lesser extent for 2017 and 2018 listings, at 25% and 39% more likely to sell, respectively. It seems that the broader fear of Alice has subsided, and buyers and sellers have figured out what to look for in patents in Alice-affected technologies.


As a buyer, tracking the behaviours of sellers – both in aggregate and individually – enables you to operationalise your buying activities. Knowing who is willing to sell and the type of assets not only allows you to review their listings faster, but also provides the opportunity to make a direct approach for a private deal. This is especially true for repeat sellers, who account for 48% of the transactions of packages in calendar years 2018 and 2019. Keeping track of a seller’s listings, package sizes and asking prices can also help in negotiations, as you know their negotiation parameters before you sit down at the table.

Similarly, if you are a seller, it is important to get the word out that you are selling. Working with brokers and listing packages on your website, through the IAM Market and via targeted email blasts will help to attract buyers to you, rather than you having to spend time and effort to find them.

For an analysis of current sellers and buyers, we looked at all of the packages that sold between 1 January 2018 and 31 May 2019 (assignments were last checked on 5 August 2019), regardless of their listing date. Operating companies continue to be the primary source of transactions – unsurprisingly, considering that those companies file the majority of patents – and were the sellers in 63% of transactions (see Figure 14). This is only a slight drop from 67% to 66% in the previous two papers. Last year’s analysis had 23 repeat sellers accounting for 41% of the sold packages; this year, 22 repeat sellers account for 47% (see Table 5).

An operating company that is buying to mitigate risk may want to monitor sellers in order to determine who is starting to sell before they fully ramp up their sales programme. Ask yourself: “How can I mitigate the risk of these patents without purchasing them?” Taking an early licence to a seller’s portfolio may be significantly less expensive than taking a licence after the assets have sold. In addition, solutions such as the License on Transfer Network may help to mitigate NPE risk across companies that currently have no intention of selling assets.

Table 6. Table 6. Repeat buyers (sold in 2018 or 2019)

Akoloutheo, LLC

Aptiv Technologies Limited

Bartonfalls LLC

Blackbird Tech LLC

Commstech LLC

Cria, Inc

Etsy Inc

Google LLC

IDL Concepts, LLC

Innobrilliance, LLC

Interdigital Ce Patent Holdings


Allied Security Trust

New Luck Global Limited

Open Invention Network LLC

Optima Direct, LLC

Pathunt IP Management Ltd

Prosper Technology, LLC

Radioxio, LLC

Red Dragon Innovations, LLC

Red Hat, Inc

Rondevoo Technologies, LLC

RPX Corporation

Sovereign Peak Ventures, LLC

Spreadtrum Communications (Shanghai) Co, Ltd

Telefonaktiebolaget LM Ericsson

Uniloc Luxembourg SA

Wi-Fi One, LLC


Figure 14. Distribution of seller type by sale year 2018-2019

Figure 14. Distribution of seller type by sale year 2018-2019

Figure 15. Distribution of buyer type by sale year 2018-2019

Figure 15. Distribution of buyer type by sale year 2018-2019


It is just as important for sellers to track buyers. While it is less obvious that buyers should also be tracking other buyers, doing so can give you a competitive advantage, as you will know whether any of your competitors or other operating companies of concern are actively buying in the market.

The percentage of packages purchased by NPEs has increased every year since 2016 (see Figure 16). This is not an anomaly; it is a trend. We may be on the cusp of a new wave of NPE activity. Not only are NPEs buying the plurality of 2018 and 2019 sold deals at 48% (see Figure 14), they are also creeping up on the majority. Operating companies should revisit their risk models with a focus on smaller litigations from multiple entities in addition to larger NPEs.

Defensive aggregator purchases, on the other hand, are lagging behind. Defensive aggregator buying dropped to 12%, down from 19% last year (see Figure 15). Operating companies have remained consistent in their buying share at 39%.

Throughout 2018 and 2019, 124 buyers purchased 227 packages, and 30 buyers purchased multiple packages (see Table 6). Last year’s analysis had 30 repeat buyers accounting for 59% of the packages purchased; this year is exactly the same. As with all our analysis, the buyer and seller analysis includes only the brokered patent market and does not include private purchases or the sales of Provenance IP Group.

Not only have we seen an increase in NPE buying, we have also seen an increase in litigation activity. The brokered patent market represents a large pool of litigation risk. Purchased patents do not sit unused; 17.6% of packages sold contain at least one US asset that went on to be litigated. Last year, this number was 16.3%. Some of this increase can be attributed to the fact that more time has passed for older packages, but many newly sold packages have been litigated.

NPEs that buy patents are certainly using them. When looking at the sold packages that were litigated after their listing date, 1,752 litigations have been filed. Of these, 90% were litigated by an NPE and the average number of NPE-filed litigations for these packages was 11.5. We only have visibility into filed cases, so these numbers do not include private assertions and licensing deals that were settled before litigation was filed.

As litigations increase, so do inter partes reviews. The same sold packages under litigation are six times more likely to be subject to an inter partes review after listing than unsold packages. Unsurprisingly, if the patents were litigated, they were subject to an inter partes review 34% of the time. This percentage does not change if we limit ourselves to NPE litigations of sold packages. Basically, sold patents are being asserted and inter partes reviews are the tool to fight back against this.

Table 7. Litigation and inter partes review frequency


Litigations (2012-2018 market year packages)

Inter partes reviews (2014-2018 market year packages)

Package type

Before listing date

After listing date


Before listing date

After listing date


Sold packages







Unsold packages







All packages







Figure 16. Percent of sales to NPEs by listing year

Figure 16. Percent of sales to NPEs by listing year

Table 8. Summary of the 2019 data

Annual sales

$300 million

Asking price per US-issued patent


Asking price per patent asset


Package sales rate (cy projected)


Number of people employed as brokers


Sold package litigation rate (tt)


Unsold package litigation rate (tt)


All package litigation rate (tt)


Brokered packages listed


US-issued patents


Patent assets


Average number of assets per package


Median number of assets per package


Packages with 10 or fewer US-issued patents


All data is 2019 market year June 2018 to May 2019 unless noted. Calendar year (cy) 2018. Total tracking (tt) – listed June 2012 to May 2019

Full market size

It appears that the market size increase in 2018 did not hold for 2019. We estimate the 2019 market size to be $300 million, down from last year’s estimated $353 million. While this drop is significant, the market has remained larger than our predictions for 2015, 2016 and 2017 ($233 million, $165 million and $296 million, respectively). Further, a significant portion of the fall can be attributed to a shift to other buying streams (eg, private deals and fixed-fee auctions) and is not a reduction of the secondary patent market, only the brokered market.

To make this estimate, we used the observed sales that occurred in the 2019 market year and their actual asking prices. As is consistent with the previous two years, if no pricing guidance was provided, the average asking price per asset for the market year of the sale (eg, $194,000 for 2019 market year sales) was multiplied by the number of assets to determine the expected asking price. As discussed previously, we used the average pricing rather than the median to determine the market size because we were modelling across many independent events, rather than identifying a typical instance. Even so, we still had to account for a few large deals. These large-deal adjustments in our calculation constitute approximately 40% of the drop from last year. In the 2019 market year, 152 sales were identified, accounting for a total asking price of $449 million. Due to recordation delays, we assume that we have not yet seen 3% of the sales that occurred in the timeframe, so we multiplied this total asking price by 1.03 before applying our standard 35% discount between asking price and expected selling price. Thus, our expected total market size for the 2019 market is $300 million.

We then used the market size (after eliminating “for sale by owner” listings) to back-calculate the number of working brokers that this market supports. By applying an average commission rate of 15%, the revenue from the market for brokers is $37 million. Last year, we used 20% for broker commission, but decided to reduce this percentage as large deals often have lower commission rates. By estimating the average loaded labour rate per broker ($300,000 a year), we calculated that there are 125 full-time equivalent employees working as brokers. Assuming that three brokers work in each brokerage, this results in approximately 42 brokerages. Our data shows 48 brokerages that listed packages in the 2019 market.

Tools and processes used to analyse the data

As the brokered patent market has matured, access to data has increased. However, the landscape remains fairly opaque. Therefore, our analysis pulls data from many sources, combining this with a proprietary set of tools that we have designed in-house.

These data sources include our proprietary patent package database, the USPTO patent data (Public-Pair), the USPTO assignment database, Cipher, Derwent Innovation, PatSnap and litigation data from DocketNavigator.

This data is then combined on both a per-patent and per-package basis, using tools that we have developed over the past five years. The result is a proprietary database of hundreds of thousands of records across nearly 500 fields. These tools are programmed in SQL, R, Ruby, AppleScript and VBA using ODBC to retrieve up-to-the-minute live data from our database. We also use business intelligence tools such as Tableau and continue to expand our capabilities to sort, sift and visualise the data.

In addition, we internally track asking prices, bidding dates and clients’ specific diligence decisions, and maintain a list of unique entities that are buying and selling with standardised names. We even classify these entities by entity type, which means that we have our own internal list of companies that we believe to be NPEs. Although this process is quite time consuming, using real data to back up our conclusion is the best way to provide accurate analyses to our clients and lower the barrier to entry for companies joining the market.

Opportunities, conclusions and reflections

The constant theme in eight years of analysing the secondary patent market is that it is always changing. However, there are always opportunities for buyers to make good deals that support their business needs and for sellers to successfully monetise. In 2018 the market exploded in size but asking prices dropped by 30%. This year, prices came roaring back up 56%, but the market dropped in size. New buying methods and private deals are becoming increasingly common and for the past four years NPEs have been buying a larger share of packages. EOUs continue to garner a premium, sold packages are litigated at higher rates and Alice-affected technologies are selling better than the rest. As market participants, we expect change; this is why data-informed strategy is so important.

The ability to adapt to this fluid market is how you stay ahead of the curve. Deeper data analytics enable you to be proactive; as a seller, for example, it is important to ask questions beyond: “Did the average price per asset change?” A better question might be: “Within my technology area, what size package should I bring to market to maximise the price per asset and sales rate?” With each year we have more data at our disposal and can ask and answer more complicated questions. The secondary market is robust, if not raucous. For those that put in the work, it continues to offer opportunities.

Action plan

The brokered patent market can appear daunting to newcomers, making it all the more important to understand the underlying data. Here are some key takeaways from this year’s report:

  • Overall, the market shrank to about $300 million from $353 million last year. Despite the drop, it remained ahead of 2016 and 2017.
  • The total volume of transactions decreased for the first time since 2016.
  • While the market fell, the average asking price jumped 56%, suggesting that we may be seeing more favourable conditions for sellers.
  • Regardless of the asking price, value should always be evaluated in context.
  • Broader fears around the impact of the US Supreme Court’s Alice decision have subsided, suggesting that buyers and sellers have figured out what to look for.
  • NPE purchases have increased every year since 2016 and we may be on the cusp of a new wave of assertion activity.

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