Task: Table-QA (source dataset: FinQA)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| $ in millions     | as of december 2015   |
|:------------------|:----------------------|
| 2016              | $ 317                 |
| 2017              | 313                   |
| 2018              | 301                   |
| 2019              | 258                   |
| 2020              | 226                   |
| 2021 - thereafter | 1160                  |
| total             | $ 2575                |

Question:
what percentage of future minimum rental payments are due in 2018?


pre_text: the goldman sachs group , inc .
and subsidiaries notes to consolidated financial statements commercial lending .
the firm 2019s commercial lending commitments are extended to investment-grade and non- investment-grade corporate borrowers .
commitments to investment-grade corporate borrowers are principally used for operating liquidity and general corporate purposes .
the firm also extends lending commitments in connection with contingent acquisition financing and other types of corporate lending as well as commercial real estate financing .
commitments that are extended for contingent acquisition financing are often intended to be short-term in nature , as borrowers often seek to replace them with other funding sources .
sumitomo mitsui financial group , inc .
( smfg ) provides the firm with credit loss protection on certain approved loan commitments ( primarily investment-grade commercial lending commitments ) .
the notional amount of such loan commitments was $ 27.03 billion and $ 27.51 billion as of december 2015 and december 2014 , respectively .
the credit loss protection on loan commitments provided by smfg is generally limited to 95% ( 95 % ) of the first loss the firm realizes on such commitments , up to a maximum of approximately $ 950 million .
in addition , subject to the satisfaction of certain conditions , upon the firm 2019s request , smfg will provide protection for 70% ( 70 % ) of additional losses on such commitments , up to a maximum of $ 1.13 billion , of which $ 768 million of protection had been provided as of both december 2015 and december 2014 .
the firm also uses other financial instruments to mitigate credit risks related to certain commitments not covered by smfg .
these instruments primarily include credit default swaps that reference the same or similar underlying instrument or entity , or credit default swaps that reference a market index .
warehouse financing .
the firm provides financing to clients who warehouse financial assets .
these arrangements are secured by the warehoused assets , primarily consisting of consumer and corporate loans .
contingent and forward starting resale and securities borrowing agreements/forward starting repurchase and secured lending agreements the firm enters into resale and securities borrowing agreements and repurchase and secured lending agreements that settle at a future date , generally within three business days .
the firm also enters into commitments to provide contingent financing to its clients and counterparties through resale agreements .
the firm 2019s funding of these commitments depends on the satisfaction of all contractual conditions to the resale agreement and these commitments can expire unused .
letters of credit the firm has commitments under letters of credit issued by various banks which the firm provides to counterparties in lieu of securities or cash to satisfy various collateral and margin deposit requirements .
investment commitments the firm 2019s investment commitments of $ 6.05 billion and $ 5.16 billion as of december 2015 and december 2014 , respectively , include commitments to invest in private equity , real estate and other assets directly and through funds that the firm raises and manages .
of these amounts , $ 2.86 billion and $ 2.87 billion as of december 2015 and december 2014 , respectively , relate to commitments to invest in funds managed by the firm .
if these commitments are called , they would be funded at market value on the date of investment .
leases the firm has contractual obligations under long-term noncancelable lease agreements for office space expiring on various dates through 2069 .
certain agreements are subject to periodic escalation provisions for increases in real estate taxes and other charges .
the table below presents future minimum rental payments , net of minimum sublease rentals .
$ in millions december 2015 .
post_text: rent charged to operating expense was $ 249 million for 2015 , $ 309 million for 2014 and $ 324 million for 2013 .
operating leases include office space held in excess of current requirements .
rent expense relating to space held for growth is included in 201coccupancy . 201d the firm records a liability , based on the fair value of the remaining lease rentals reduced by any potential or existing sublease rentals , for leases where the firm has ceased using the space and management has concluded that the firm will not derive any future economic benefits .
costs to terminate a lease before the end of its term are recognized and measured at fair value on termination .
176 goldman sachs 2015 form 10-k .
label: 0.11689000000000001
qa: answer: 12%
ann_table_rows: 3
7
steps: op: divide2-1
arg1: 301
arg2: 2575
res: 12%
program: divide(301, 2575)
gold_inds: table_3: $ in millions the 2018 of as of december 2015 is 301 ;
table_7: $ in millions the total of as of december 2015 is $ 2575 ;
exe_ans: 0.11689000000000001
tfidftopn: text_25: the table below presents future minimum rental payments , net of minimum sublease rentals .
program_re: divide(301, 2575)
model_input: table_1
$ in millions the 2016 of as of december 2015 is $ 317 ;
table_3
$ in millions the 2018 of as of december 2015 is 301 ;
table_7
$ in millions the total of as of december 2015 is $ 2575 ;